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Typo hosted an exclusive live webinar titled 'The Hows and Whats of DORA', featuring Bryan Finster and Richard Pangborn. With over 150+ attendees, we explored how DORA can be misused and learnt practical tips for turning engineering metrics into dev team success.
Bryan Finster, Value Stream Architect at Defense Unicorns and co-author of 'How to Misuse and Abuse DORA Metrics’, and Richard Pangborn, Software Development Manager at Method and advocate for Typo, brought valuable perspectives to the table.
The discussion covered DORA metrics' implementation and challenges, highlighting the critical role of continuous delivery and value stream management. Bryan provided insights from his experience at Walmart and Defense Unicorns, explaining the pitfalls of misusing DORA metrics. Meanwhile, Richard shared his hands-on experience with implementation challenges, including data collection difficulties and the crucial need for accurate observability. They also reinforced the idea that DORA metrics should serve as health indicators rather than direct targets. Bryan and Richard offered parting advice on using observability effectively and ensuring that metrics lead to meaningful improvements rather than superficial compliance. They both emphasized the importance of a supportive culture that sees metrics as tools for improvement rather than instruments of pressure.
The event concluded with an interactive Q&A session, allowing attendees to ask questions and gain deeper insights.
P.S.: Our next live webinar is on September 25, featuring DORA expert Dave Farley. We hope to see you there!
Kovid Batra: Hi, everyone. Thanks for joining in for our DORA exclusive webinar, The Hows and Whats of DORA, powered by Typo. I'm Kovid, founding member at Typo and your host for today's webinar. With me, we have two special people. Please welcome the DORA expert for tonight, Bryan Finster, who is an exceptional Value Stream Architect at Defense Unicorns and the co-author of the ebook, 'How to Misuse and Abuse DORA Metrics', and one of our product mentors, and Typo advocates, Richard Pangborn, who is a Software Development Manager at Method. Thanks, Bryan. Thanks, Rich, for joining in.
Bryan Finster: Thanks for having me.
Richard Pangborn: Yeah, no problem.
Kovid Batra: Great. So, like, before we, uh, get started and discuss about how to implement DORA, how to misuse DORA, uh, Rich, you have some questions to ask, uh, we would love to know a little bit about you both. So if you could just spare a minute and tell us about yourself. So I think we can get started with you, Rich. Uh, and then we can come back to Bryan.
Richard Pangborn: Sure. Yeah, sounds good. Uh, my name is Richard Pangborn. I'm the Software Developer Manager here at Method. Uh, I've been a manager for about three years now. Um, but I do come from a Tech Lead role of five or more years. Um, I started here as a junior developer when we were just in the startup phase. Um, went through the series funding, the investments, the exponential growth. Today we're, you know, over a 100-person company with six software development teams. Um, and yeah, Typo is definitely something that we've been using to help us measure ourselves and succeed. Um, some interesting things about myself, I guess, is I was part of the company and team that succeeded when we did a Intuit hackathon. Um, it was pretty impactful to me. Um, We brought this giant check, uh, back with us from Cali all the way to Toronto, where we're located. Uh, we got to celebrate with, uh, all of the company, everyone who put in all the hard and hard work to, to help us succeed. Um, that's, that's sort of what pushed me into sort of a management path to sort of mentor and help those, um, that are junior or intermediate, uh, have that same sort of career path, uh, and set them up for success.
Kovid Batra: Perfect. Perfect. Thanks, Richard. And something apart from your professional life, anything that you want to share with the audience about yourself?
Richard Pangborn: Uh, myself, um, I'm a gamer, um, I do like to golf, I do like to, um, exercise, uh, something interesting also is, um, I met my, uh, wife here at the company who I still work with today.
Kovid Batra: Great. Thank you so much, Rich. Bryan, over to you.
Bryan Finster: Oh, yes. I'm Bryan Finster. I've been a software developer for, oh, well, since 1996. So I'll let you do the math. I'm mostly doing enterprise development. I worked for Walmart for 19 of those years, um, in logistics for most of that time and, uh, helped pilot continuous delivery at Walmart inside logistics. I've got scars to show for it. Um, and then later moved to platform at Walmart, where I was originally in charge of the delivery metrics we were gathering to help teams understand how to do continuous delivery so they can compare themselves to what good continuous delivery looked like. And then later was asked to start a dojo at Walmart to directly pair with teams to help them solve the problem of how do we do CD. And then about a little over three years ago, I was, I joined Defense Unicorns as employee number three of three, uh, and we're, we're now, um, over 150 people. We're focused on how do we help the Department of Defense deliver, um, you know, do continuous delivery and secure environments. So it's a fun path.
Kovid Batra: Great, great. Perfect. And the same question to you. Something that LinkedIn doesn't tell about you, you would like to share with the audience.
Bryan Finster: Um, computers aren't my hobby. Uh, I, you know, it's a lot better than roofing. My dad had a construction company, so I know what that's like. Um, but I, I very much enjoy photography, uh, collecting watches, ride motorcycles, and build plastic models. So that's where I spend my time.
Kovid Batra: Nice. Great to know that. All right. So now I think, uh, we are good to go and start with the main section of, of our webinar. So I think first, uh, let's, let's start with you, Bryan. Um, I think you have been a long-time advocate of value streams, continuous delivery, DORA metrics. You just briefly told us about how this journey started, but let's, let's deep dive a little bit more into this. Uh, tell us about how value stream management, continuous delivery, all this as a concept started appealing to you from the point of Walmart and then how it has evolved over time for you in your life.
Bryan Finster: Sure. Uh, no, at Walmart, um, continuous delivery was the answer to a problem. It wasn't, it was, we had a business problem, you know, our lead time for change in logistics was a year. We were delivering every quarter with massive explosions. Every time we piloted, I mean, it was really stressful. Um, any, anytime we did a big change of recorder, we had planned 24 by 7 support for at least a week and sometimes longer, um, And it was just a complete nightmare. And our SVP, instead of hiring in a bunch of consultants, cause we've been through a whole bunch of agile transformations over the years, asked the senior engineers in the area to figure out how we can deliver every two weeks. Now, if you can imagine these giant explosions happening every two weeks instead of every quarter, we didn't want that. And so we started digging in, how do we get that done? And my partner in crime bought a copy of continuous delivery. We started reading that book cover to cover, pulling out everything we could, uh, started building Jenkins pipelines with templates, so the teams didn't have to go and build their own pipeline. They can just extend the base template which was a pattern we took forward later. And, and, uh, we built a global platform. I started trying to figure out how do we actually do the workflow that enables continuous delivery. I mean, we weren't testing at all. Think how scary that is. Uh, other than, you know, handing it off to QA and say, "Hey, test this for us.
And so I had to really dig into how do we do continuous integration. And then that led into what's the communication problems that are stopping us from getting information so we can test before we commit code. Um, and then once you start doing that at the team level, what's preventing us from getting all the other information that we need outside the team? How do we get the connection? You know that, all the, all the roadblocks that are preventing us from doing continuous delivery, how do we fix those? Which kind of let me fall backwards in the value stream management because now you're looking at the broader value stream. It's beyond just what your team can do. Um, and so it's, uh, it's, it's been just a journey of solving that problem of how do we allow every team to independently deploy from any other team as frequently as they can.
Kovid Batra: Great. And, and how do, uh, DORA metrics and engineering metrics, while you are implementing these projects, taking up these initiatives, play a role in it?
Bryan Finster: Well, so, you know, all this effort that we went on predated Accelerate coming out, but I was going to DevOps Enterprise Summit and learning as much as I could starting in 2015 and talking to people about how do we measure things, cause I was actually sent to DevOps Enterprise Summit the first time to figure out how do we measure if we're doing it well, and then started pulling together, you know, some metrics to show that we're progressing on this path to CD, you know, how frequently integrating code, how many defects are being generated over time, you know, and how, how often can individuals on the team deploy as like, you know, deploys per day per developer was a metric that Jim proposed back in 2015 as just a health metric. How are we doing? And then later in the, and when we started the dojo in platform at Walmart, we were using a metrics-based approach to help teams. Continuous delivery was the method we were using to improve engineering excellence in the organization. We, you know, we weren't doing any Agile frameworks. It was just, why can't we deliver change daily? Um, and early on when we started building the platform, the first tool was the CI tool. Second tool was how do we measure. And we brought in CapitalOne's Hygieia, and then we gamified delivery metrics so we can show teams with a star rating how they were doing on integration frequency, build time, build success rate, deploy frequency, you know, and code complexity, that sort of thing, to show them, you know, this is what good looks like, and here's where you are. That's it. Now, I learned a lot from that, and there's some things I would still game today, and some things I would absolutely not gamify. Um, but that's where I, you know, I spent a long time running that as the game master about how do we, how do we run the game to get teams to want to, want to move and have shown where to go.
And then later, Accelerate came out, and the big thing that Accelerate did was it validated everything we thought was true. All the experiences we had, because the reason I'm so passionate about it is that first, first experience with CD was such a morale improvement on the team that I, nobody ever wanted to work any other way, and when things later changed, they were forced to not work that way by new leadership, everyone who could left. And that's just the reality of it. And, but accelerate came out and said these exact things that we were seeing. And it wasn't just a one-off. It wasn't just this, you know, just localized to. What we were saying, it was everywhere.
Kovid Batra: Yeah, totally makes sense. I think, uh, it's been a burning topic now, and a lot of, uh, talks have been around it. In fact, like, these things are at team-level, system-level. In fact, uh, I'm, uh, the McKinsey article that came out, uh, talking about dev productivity also. So I, I have actually a question there. So, uh.
Bryan Finster: Oh, I shouldn't have read the article. Yeah, go ahead.
Kovid Batra: I mean, it's basically, it's basically talking about individual, uh, dev productivity, right? People say that it can be measured. So yeah. What's your take on that?
Bryan Finster: That's, that's really dumb. If you want to absolutely kill outcomes, uh, focus on HR metrics instead of outcome metrics, you know. And, and so, I want to touch a little bit on the DORA metrics I think. You know, I've, having worked to apply those metrics on top of the metrics we're already using, there's some of them that are useful, but you have to understand those came from surveys, and there's some of them that are, that if you try to measure them directly, you won't get the results you want, you won't get useful data from measuring directly. Um, you know, and they don't tell you things are going well, they only tell you things are going poorly and you can't use those as your, your, the thing that tells you whether, whether you're delivering value well, you know? It's just something that you, cause you to ask questions about what might be going wrong or not, but it's not, it's not something you use like a dashboard.
Kovid Batra: Makes sense. And I think, uh, the book that you have written, uh, 'How to Misuse and Abuse DORA Metrics', I think, let's, let's talk, talk about that a little bit. Like you have summarized a lot of things there, how DORA metrics should not be used, or Engineering metrics for that matter should not be used. So like, when do you think, how do you think teams should be using it? When do the teams actually feel the need of using these metrics and in which areas?
Bryan Finster: Well, I think observability in general is something people don't pay enough attention to. And not just, you know, not just production observability, but how are we working as a team. And, and really what we're trying to do is you have to think of it first from what are we trying to do with product development. Um, a big mistake people make is assuming that their idea is correct, and all we have to do is build something according to spec, make sure it tests according to spec and deliver it when we're done. When fundamentally, the idea is probably wrong. And so the question is, how big of a chunk of wrong idea do I want to deliver to the end user and which money do I want to spend doing that? So what we're trying to do is we're trying to become much more efficient about how we make change so we can make smaller change at lower costs so that we can be more effective about delivering value and deliver less wrong stuff. And so what you're really trying to do is you're trying to measure the, the, the way that we work, the way we test, to find areas where we can improve that workflow, so that we can reduce the cost and increase the velocity, which we can deliver change. So we can deliver smaller units of work more frequently, get faster feedback and adjust our idea, right? And so if you're not, if you're just looking at, "Oh, we just need to deliver faster." But you're not looking at why do we want to deliver faster is to get faster feedback on the idea. And also from my perspective, after 20 years of carrying a pager, fix production very, very quickly and safely, I think those are both key things.
And so what we're trying to do with the metrics is we're trying to identify where those problems are. And so in the paper I wrote for IT revolution, which was about twice as long as they asked me for on, on how to misuse and abuse DORA metrics, I went into the details of how we apply those metrics in real life. At Walmart, when we were working with teams to help them improve and also, you know, using them on ourselves, I think if a team really wants to focus on improving, the first thing they should measure is how well they're doing at continuous integration, you know, how frequently are we integrating code, how long does it take us to finish whatever a unit of work is, and what's our, uh, how many defects we're generating, uh, over time as a trend. And measure trends and improve all those trends at the same time.
Kovid Batra: How do we measure this piece where we are talking about measuring the continuous integration?
Bryan Finster: So, as an average on the team, how frequently are we integrating code? And you really want to be at least daily, right? And that's integrated to the trunk, not to some develop branch. And then also, you know, people generally work on a task or a story or whatever it is. How long does it take to go from when we start that work until it's delivered? What's that time frame? And there's, there's other times within that we can measure and that was when we get into value stream mapping. We can talk about that later, but, uh, we want small units of work because you get higher quality information if you get smaller units work and you're more predictable on delivery of that unit of work, which takes a lot of pressure off, it eliminates story points. But then you also have to balance those with the quality of what we did, and you can't measure that quality until it's in production, because test to spec doesn't mean it's good. 'Fit for purpose' means the user finds it good.
Kovid Batra: Right. Can you give us some examples of where you have seen implementing these metrics went completely south instead of working positively? Like how exactly were they abused and misused in a scenario?
Bryan Finster: Yeah, every single time somebody builds a dashboard without really understanding what the problems you're trying to solve are, I see, I've seen lots of people over the years since Accelerate was published, building dashboards to sell, but they don't understand the core problem they're trying to solve. But also, you know, when you have management who reads a book and says, Oh, look, here's an end, you know, I helped cause this problems, which is why I work so hard to fix it by saying, "Hey, look at these four key metrics." Aren't you? You know, this, this can tell you some things, but then they start using them as goals instead of health indicators that are contextual to individual teams. And when you start saying, "Hey, all teams must have this, this level of delivery frequency." Well, maybe, but everybody has their own delivery context. You're not going to deliver to an air-gapped environment as frequently as you are to, you know, AWS, right? And so, you have to understand what it is you're actually trying to do. What, what decisions are you going to make with any metric? What questions are you trying to answer before you go and measure it? You have to define what the problem is before you try to measure that you're successful at correcting the problem.
Kovid Batra: Right. Makes sense. There are challenges that I've seen in teams. Uh, so of course, Typo is getting implemented in various organizations here. What we have commonly come across is teams tend to start using it, but sometimes it happens that when there are certain indicators highlighted from those metrics, they're not sure of what to do next.
Bryan Finster: Right.
Kovid Batra: So I'm sure you must.
Bryan Finster: Well, but the reason why is because they didn't know why they were measuring it in the first place, right? And so, like I said, you know, DORA metrics in specific, they tell you something, but they're very much trailing metrics, which is why I point to CI because CI is really the, the CI workflow is really the engine that starts driving improvement. And then, you know, once you get better at that, you say, "Well, why can't I deliver today's work today?" And you start finding other things in the value stream that are broken, but then you have to identify, okay, well, We see this issue here with code review. We see this issue here. We have this handoff to another team downstream of development before we can deploy. How do we improve those? And how can we measure that we are improving? So you have to ask the question first. And then come up with the metrics that you're using to evaluate success.
And so, people are saying, well, I don't know what to do with this number. It's because they don't, they didn't, they started with a metric and then tried to figure out what to do with it because someone told him it was a good metric. No metric is a good metric unless you know what you're doing with it. I mean, if I put a tachometer on a car and you think that more is better but you don't understand what the tachometer is telling you, then you'll just blow up your engine.
Kovid Batra: But don't you think like there is a possible way to actually not know what to measure, but to identify what to measure also from these metrics itself? For example, like, uh, we have certain benchmarks for, uh, different industries for each metric, right? And let's say I start looking at the lead time, I start looking at the deployment frequency, mean time to restore, there are various other metrics. And from there, I try to identify where my engineering efficiency or productivity is, productivity is getting impacted. So can, can it not be a top-down approach where we find out what we need to actually measure and improve upon from those metrics itself?
Bryan Finster: Only if you start with a question you're trying to answer. But I wouldn't compare. So one of the other problems I have with the DORA metrics specifically is that the, and I've talked to DORA at Google about this as well, it's, it's like some of the questions are nonspecific. So for your, the system you work on most of the time, how frequently you deliver. Well, are you talking about a thousand developers, a hundred developers, a team of eight, right? And so, your delivery frequency is going to be very much relative to the number of people working on it, plus other constraints outside of it. And so you, yes, high performers deliver, you know, multiple times a day with, uh, you know, lead times of less than an hour, except that what's the definition of lead time? Well, there's two inside Accelerate, and they're different depending on how you read it. And, but that doesn't mean that you should just copy what it says. You should look at that and say, "Okay, now what, what am I trying to accomplish? And how can I apply these ideas? Not necessarily the metrics directly, but how can I apply these ideas to measure what I'm trying to measure to find out where my problems are?" So you have to deep dive into where your problems are. And so just like, "Hey, measure these things and here's your benchmarks.
Kovid Batra: Makes sense. Makes sense. Richard, do you have a point that I think we have been talking for a long, if you have any question, uh, I think let's, let's hear from Richard also. Um, he has used Typo, uh, has been using it for a while now, and I'm sure, uh, in this journey of implementing engineering metrics, DORA metrics in his team, he would have seen certain challenges. Richard, I think the stage is yours.
Richard Pangborn: Yeah, sure. Um, so my research into using DORA metrics stem from, um, building high-performing teams. So, um, we always, we're looking for continuous improvement, but we're really looking for ways to measure ourselves that, that makes sense, that can't be totally gamed, that, um, that are like standards. Uh, what I liked about DORA was it had some counterbalancing metrics like throughput versus quality, time to repair versus time to build, speed for stability. That's, it's a, it's a nice counterbalancing, um, effect. Um, and high-performing teams, they care about stuff like continuous improvement, they want to do better than they did last quarter or, or last month, they want to, um, they want help with decision-making. So better data to drive some of the guesswork about, you know, what, what area needs, um, The most improvement or what area is, uh, broken in our pipeline, maybe for like continuous delivery for quality. Um, I want to make sure that they're making a difference, that they're moving a needle, um, ladders up. So a lot of times, a lot of companies, uh, have different measurements at different levels, like company level, department level, team level, individual level. So DORA, we were able to identify some that do ladder up, which is great.
Some of the there are some challenges with implementing DORA, like when we first started. Um, so I think part of the challenge, one of the first ones was the complexity around data collection. Um, so, you know, accurately tracking and measuring DORA metrics. So deployment frequency, lead time for changes, failure rate, recovery, um, they all come from different sources. So CI/CD pipelines, version control systems, incident management tools. So integrating these data sources and ensuring they provide consistent results can be a little time consuming. Um, it can be a little difficult to understand. Yeah, so that was, that was definitely one part of it. Uh, we haven't rolled out all four yet. We're still in the process, just ensuring that, you know, what we are measuring is accurate.
Bryan Finster: Yeah, and I'm glad you touched on the accuracy thing. Um, When we would go and work with teams and start collecting data, so number one, we had data from the pipeline because it was embedded into the platform, but we also knew that when we worked with teams that the Git data was accurate, but the workload was going to be garbage unless the teams actually cared about using Jira correctly. And so, education step number one was while we were cleaning up the, the data in Jira, educating them on why Jira actually should matter to them, instead of just as a, it's not, it's not a time-tracking tool, it's a communication tool. You know, and educating them so that they would actually take it seriously so that the workflow data would be accurate so that they could then use that to help them identify where the improvements could happen because we're going to try to teach them how to improve, we weren't just trying to teach them to do what we said. Um, and yeah, I built a few data collection tools since we started this, and yeah, the collecting the data and showing where, um, accuracy problems happen as part of the dashboard is something that needs to be understood because people will just say, "Oh, the data's right." But yeah, I mean, especially with workflow data, one of the things we really did on the last one I built was show where, where the, you know, where we're out of bounds, very high or very low, you know. I talked to management. I was like, "Well, look, we're doing really good. I've got stuff closing here really fast." I'm like, you're telling me it took 30 seconds to do that, give it a work. Yeah, the accuracy issues. And MTTR is something that DORA's talked about ditching entirely because it's a far too noisy metric if you're trying to collect it automatically.
Richard Pangborn: Yeah, we haven't started tracking MTTR yet. Um, we're more concerned with the throughput versus stability that would have the biggest, um, change at the department level, at the team level. Um, I think, I think that's made the difference so far. Also, we have a challenge with, um, yeah, just doing a lot of stuff manually. So lack of tooling and automation. Um, there's a lot of manual measurements that are taking place. So like you said, error-prone for data collection, inconsistent processes. Um, once we get to a more automated state, I feel like it will be a bit more successful.
Bryan Finster: Yeah. There's a dashboard I built for the, for the Air Force. I'll send you a link later. It might, it might be useful, I'm not sure. But also the other thing is change failure rate is something that people misunderstand a lot, uh, and I've, I've combed through Accelerate multiple times. Uh, uh, Walmart has actually asked to reverse engineer the survey for the book, so I've gone back in depth. Change failure rate is any defect. It's not an incident. If you go and read what it says about change failure rate, it's any defect, which it should be because also the idea is wrong. If the user's reporting it's defective, and you say, "Well, that's a new feature." No, the idea was defective. We're not, it's not fit for purpose in most, you know, unless it's some edge case, but we should track that as well, because that's part of our quality process and change failure rate's trying to track our quality process.
Richard Pangborn: Another problem we had is, um, mean, uh, meantime to recovery. So because we track our bugs or defects differently, they have different priorities. So, um, P0s here has to be done, has to be fixed in less than 24 hours. Um, P, priority 1 means, you know, five days, priority two, you have two weeks. So trying to come up with a, an algorithm to accurately identify, um, time to fix, I guess you'd have like three, three or four different ones instead of one.
Bryan Finster: I've tried to solve that problem too, and especially on distributed systems, it becomes very difficult. So who's getting measured on MTTR? I mean, I'm sorry. Yes, yes. Who's getting measured, right? It's going to be because MTTR, by definition, is when the user sees impact. And so really, that's whoever has the user interface owns that metric. If you're trying to help a team improve their processes for recovery. So it's, it's, it's just a really difficult metric to try to do anything with unless, um, well, you can't, it's, I've, I've, I've tried to measure it directly. I've talked to Verizon, CapitalOne, uh, you know, other people in the dojo consortium, they've tried to make, nobody's been successful at measuring it. But yeah. I think better metrics are out there for how fast we can resolve defects.
Richard Pangborn: Um, one of the things we were concerned about at the beginning was like a resistance to measurement. Um, some people don't want to be measured.
Bryan Finster: That's because they have management meeting over the head and using it as, as the reason why it's a massive fear thing. And it's part of the, it's a cultural thing. I mean, as long as you, it's, you have to have a generative culture to make these metrics effective. One of the things we would do when we start working with teams is number one, we'd explain to them, we're not trying to judge you. We're like your doctor. We're working with you. We're in the trenches with you. These are all of our metrics. They're not yours. And here's how to use them to help you improve. And if a manager comes and starts trying to beat you up with them, just, you know, stop making the data valid.
Richard Pangborn: Yeah. Well, some developers do want to know am I doing well, how do I measure myself? Um, So this gives them a way to do it a little bit, but we told them, um, you know, you set your own goals. Improve yourself. Don't measure yourself next to a developer, another developer on your team or, or someone else where you're looking for your own improvement.
Bryan Finster: Well, I think it's also really important that the smallest unit that's measured with delivery metrics is team and not person. If, if, if individuals are being measured, they're going to optimize for themselves instead of optimizing for team goals. And this is something I've seen, uh, frequently, uh, there was one, uh, with, you know, on, on our, on the dojo team, we can walk into your team and see that if there was filters by individual developer, your team was seriously broken. Uh, and I've seen managers who measured team members by how many Jira issues they closed, which meant that code review is going to be delayed, uh, mentoring was not going to happen, um, uh, you'd have senior engineers focusing on easy tasks to get their numbers up instead of focusing on solving the hard problems, design was not going to happen well because it wasn't a ticket, you know, and so you focus on team outcomes and measure team goals and individual performance because everybody has different roles on the teams. People know that from an HR perspective, coaching by walking around is how you find out who's struggling. You go to the gimbal, you find out who's struggling, you can't measure people directly, that way it'll impact team goals, business goals.
Richard Pangborn: Yeah, I don't think we measure it as a, um, whether they're not successful, it's just something for them to, to watch themselves.
Bryan Finster: As long as somebody else can see it. I mean.
Richard Pangborn: Yeah, it's just for them, isn't it? Not for anyone else.
Bryan Finster: Yeah.
Richard Pangborn: Um, cool. Yeah. Yeah. That's, that's about it for me. I think at the moment.
Kovid Batra: Perfect, perfect. I think, uh, Rich, if, if you are done with your questions, we have already started seeing questions from the audience.
Bryan Finster: There's one other thing I'd like to mention real quick before we go there.
Kovid Batra: Sure.
Bryan Finster: I also gave a talk about how to misuse and abuse DORA metrics, and the fact that people think there's, yes, there's four key metrics they focus on, but read Accelerate. There's a lot more in that book for things that you should measure, including culture. Uh, it's, it's important that you look at this as a holistic thing and not just focus on these metrics to show how well we're doing at CD. Cool, but the most valuable thing in Accelerate is Appendix A and not the four key metrics. So that's number one. But number two, value stream maps, they're manual, but they give you far deeper insights into what's going wrong than the 4 key metrics will. So learn how to do value stream maps and learn how to use them to identify problems and fix those problems.
Kovid Batra: And how exactly, uh, so just an example, I'm expecting an example here, like when, when you are dealing with value stream maps, you're collecting data from system, you're collecting data from people through surveys and what exactly are you creating here?
Bryan Finster: No, I don't collect any data from the system initially. So if I'm doing a value stream map, it'll be bringing a team together. We're not doing it at the, at the organization level. We're doing it at the team level. So you bring a team together and then you talk about the process, starting from delivery and working backwards to initiation of how we deliver change. Uh, you get a consensus from the team about how long things take, how long things are waiting to start. And then you start seeing things like, Oh, we do asynchronous code review, and so I'm ready for code review to start. Four to eight hours later, somebody picks it up and they review it. And then I find out later that they've done and there's changes being made, you know, maybe the next day. And then I go make those changes, resubmit it, and like four to eight hours later, somebody would go re-review it. And, and you see things like, Oh, well, what if we just sat down and discuss the change together and just fix it on the fly, um, and remove all that wait time? How much, you know, that would encourage smaller pieces of work? And we can deliver more frequently and get faster feedback and see, you can see just immediate improvements from things like that, just by doing a value stream map. But bringing the team together will give you much higher quality data than trying to instrument that because not all of those things are, there's data being collected anywhere.
Kovid Batra: Makes sense. All right. We'll take a minute break and we'll start with the Q and A after that. So audience, uh, please shoot out all your questions that you have.
All right. Uh, we have the first question.
Bryan Finster: Yeah. So MTTR is a metric measuring customer impact. So the moment from when a customer is impacted or user impact until they are no longer impacted. And that doesn't mean you fix the defect. It means that you are no, they are no longer being impacted. So roll back, roll forward, doesn't matter. That's what MTTR has mentioned.
Kovid Batra: Perfect. Let's, let's move on to the next one.
Bryan Finster: Yeah. So, um, there's some things where I can set hard targets on as, as ways to know that we're doing well. Integration frequency is one of those, you know, if, if we're integrating once per day or better into the trunk, then we're doing a really good job of breaking down our work. We're doing a good job of testing, or as long as we keep our defects from blowing up, you know, we should be testing. But you can set targets for that. You can also set targets as a team, not something you impose on a team. This is something we as a team do that we want to keep a story size of two days or less. Paul Hammett would say one day or less. Uh, but I think two days is, is a good time limit, that if we, if it takes us more than two days, we'll start running into other dysfunctions that cause quality impact and, and issues with delivery. So I've built dashboards where I have a line on those two graphs that say "this is what good looks like", so the teams can compare themselves to good. Other things that you don't want to gamify, you don't ever want to measure test coverage and say, "Hey, this is what good test coverage looks like." Because test coverage doesn't measure quality. It just measures how much code is executed by code that says it's a test whether it's a test or not. So don't want to do that. That's a fail. I learned that the hard way. Delivery frequency, of course, it's, that's relative to their delivery problem. Uh, you may be delivering every day, every hour, every week, and that all could be good. It just depends. Um, but you can make objective measurements on integration frequency and how long a unit of work takes to do.
Kovid Batra: Cool. Moving on to the next one. Uh, any recommendations where you learn, uh, where we can learn value stream maps?
Bryan Finster: Yeah, so Steve Pereira and Andrew Davis released 'Flow Engineering', which is basically, because there's lots of books on value stream mapping, but it's, from the past, but they're mostly focused on manufacturing and Steve and Andrew released the Flow Engineering book where they talk about using value stream maps to identify problems and how to go about fixing those things. So it was just released earlier this year.
Kovid Batra: Cool. Moving on to the next one. When would you start and how to convince upper management? They want KPI now and we are trying to get a VSM expert to come in and help. It's a hard sell.
Bryan Finster: Yeah, yeah. We want easy numbers. Okay. Well, you know, I would, I would start with having a conversation about what problems we're trying to solve. It's very much like the conversation you have when you're trying to convince management that we want to do continuous delivery. They don't care about continuous delivery unless that they're, they're deep into the topic. But they do care about, uh, you know, delivering better about business value. So you talk about the business value. When you're talking about performance indicators, well, what performance are we trying to measure? And we really need to have that hard conversation about, are we trying to measure how much, how many lines of code are getting dumped onto the end user? How much value are we delivering? Are we trying to, you know, reduce the size and cost of delivering change so we can be more effective about this, or are we just trying to make sure people are busy? And so if you have management that just wants to make sure people are productive, uh, and they're not opening to listening to why they're wrong, I'd quit.
Kovid Batra: All right. Can we move on to the next one then?
Bryan Finster: Where's the next one?
Kovid Batra: Yeah.
Bryan Finster: Oh, okay.
Kovid Batra: Is there any scientific evidence we can use to point out that working on small steps iteratively is better than working in larger batches? The goal is to avoid anecdotal evidence while discussing what can improve the development process.
Bryan Finster: You know, the hard thing about software, uh, in an industry is that people don't like sharing their information, uh, the real information because it can be stock impacting. And so we're, we're going to get a scientific study from a private company. Um, but we have a, you know, a few centuries worth of, of knowledge about knowing that if you build a whole bunch of the wrong thing, that you're not going to sell it. Um, there's, you don't have to do a scientific study because we have knowledge from manufacturing. Uh, you know, the, the, the Simpsons, the documentary The Simpsons, where they talk about the Homer car, where they build the entirely wrong car and put the company out of business without, because there was no feedback loop on that car at all until it was unveiled. Right? That's, that's really the problem. We're doing product development. And if you go off and say, I have this brilliant, well, you know, like, uh, uh, what was the, uh, Silicon Valley, they spent so much money building something nobody wanted and they kept iterating and trying to find the right thing, but they kept building the complete thing and building the wrong thing and just burning money. And this, this is the problem we're trying to solve. And so you're, you're trying to get faster feedback about when we're wrong, because we're inventing something new. Edison didn't build a million wrong light bulbs and see if any, I see if they worked.
Kovid Batra: All right. I think we can move on to the next one. Uh, what strategies do you recommend for setting realistic yet ambitious goals based on our current DORA metrics?
Bryan Finster: Uh, I would start with why can't we deliver today's work today? Well, I'd do that right after why can't we integrate today's work today? And then start finding out what those problems are and solving them. Uh, as far as ambitious goals, I mean, I think it's ambitious to be doing continuous delivery. Why can't we do continuous delivery? Uh, you know, one of the reasons why we put minimumcd. org together several years ago was because it's a list of problems to solve, and if you solve those problems, you can't solve those problems with an organization that's not a great place to work. You just can't. And the goal is to make it a better place to work. So solve those problems. That's an ambitious goal. Do CD.
Kovid Batra: Richard, do you have a question?
Richard Pangborn: Uh, myself? No?
Kovid Batra: Yup.
Richard Pangborn: Nope.
Kovid Batra: Okay. One last one we'll take here. Uh, yeah.
Bryan Finster: Yeah, so common pitfalls, and I think we touched on some of these before, is trying to instrument all but two of them. You could instrument two of them mostly, I think that, uh, you know, and change fail rate is not named well because of the description. It's really defect arrival rate. But even then, that depends on being able to collect data from defects and whether or not that's being collected in a disciplined manner. Um, delivery frequency, you know, people frequently measure that at the organization level, but that doesn't really tell you anything. You really need to get down to where the work is happening and try to measure that there. But then setting targets around delivery frequency, instead of identifying how do we improve, right? And it's, it's, it's all it is, is how do we, how do we get better, um, using them as goals? They're absolutely not goals. They're health indicators. You know, like I talked about the tachometer before, I don't have a goal of, we're going to run at 5, 000 RPM. I mean, number one, it depends on the engine, right? I mean, that would be really terrible for a sport bike, would blow up a diesel. So we, we need to, using them naively without understanding what they mean and what it is we're trying to do. I see it constantly. Uh, I and others who were early adopters of these met out, screaming about this for several years, and that's why I'm on here today is please, please don't use them incorrectly because it just hurts things.
Kovid Batra: Perfect. Uh, Bryan, I have one question. Uh, uh, like when, when teams are setting these benchmarks for different metrics that they have identified to be measured, what should be the ideal strategy, ideal way of setting those benchmarks? Because that's a question I get asked a lot.
Bryan Finster: Let's say, they were never benchmarks in Accelerate either. What they said was is that we're seeing a correlation between companies with these outcomes and metrics that look like this. So those aren't industry benchmarks, that's a correlation they're making. And correlation is not equal causation. I will tell you that being really good at continuous delivery means that you can, if you have good ideas, deliver good ideas well, but being good at CD doesn't mean you're going to be good at, at, at, you know, meeting your business goals because it depends, you know, garbage in, garbage out. Um, and so, you don't set them as benchmarks. They're not benchmarks. They're health indicators. Use them as health indicators. How do we make this better? Use them as, as things to cause you to ask questions. Why can't we deliver more than once a month?
Kovid Batra: So basically, if we are, let's say, for a lack of a better term, we use 'benchmarks'. There should, those should be set on the basis of the cadence of our own team, how they are working, how they are designed to deliver. That's how we should be doing. Is that what you mean?
Bryan Finster: No, I would absolutely use them as health indicators, you know, track trends. Are we trending up? Are we trending down? And then use that as the basis of starting an investigation into why are we trending up? Why are we trending down? I mean, are we trending up because people think it's a goal? And were there some other metric that's going south that we're not aware of while we're, while we're focusing on this one thing getting better? I mean, this is Richard, I mean, you pointed out exactly. It's a good balance set of metrics if they're measured correctly unlike if it's collected correctly. And you can't, you know, another problem I see is people focusing on 1. I remember a director telling his area, "Hey, we're going to start using DORA metrics. But for change management purposes, we're only going to start by focusing on MTTR instead of anything else." They're a set, they go together, you know? You can't just peel one out. Um, so.
Kovid Batra: Got it, got it. Yeah, that absolutely answers my question. All right. I think with that, we come to the end of this session. Uh, before we part, uh, any parting advice from you, Bryan, Rich?
Richard Pangborn: Um, just what we found successful in our own journey. Every, every company is different. They all have their own different processes, their own way of doing things, their own way of building things. So, there's not exactly one right way to do it. It's usually by trial and error for each, probably each company, uh, I would say. Depending on the tooling that you want to choose, the way you want to break down tasks and deliver stories. Like for us, we chose one day tasks in Jira. Um, we didn't choose, uh, long-lived branches. Um, we're not trunk-based explicitly, but we're, our PRs last no longer than a day. Um, so this is what we find works well for us. We're delivering daily. We haven't gotten yet to the, um, you know, delivering multiple times a day, but that's, that's somewhere in the future that we're going to get to, but you have to balance that with business goals. You need to get buy-in from stakeholders before you can get, um, development time to sort of build out that, that structure. So, um, it's a process. Um, everyone's different. Um, but I think bringing in some of these KPIs or, or sorry, benchmarks or health metrics, whatever you want to call them, um, has worked for us in the way where we have more observability into how we operate as engineers than we've ever had in the past. Um, so it's been pretty beneficial for us.
Bryan Finster: Yeah. I'd say that the observability is critical. Um, you know, I've, I've built a few dashboards for showing these things. And for people, for development teams who were, uh, focusing on "we want to improve", they always found value in those things. Um, but I, one, one caution I have is that if you are showing metrics on a dashboard, understand that the user experience of that will change people's behaviors. It's so important people understand. And whenever I'm building a dashboard, I'm showing offsetting metrics together in a way that they can't be separated, um, because you, otherwise you'll just focus on one. I want you to focus on those offsetting metrics as a group, make them all better. Um, but it only matters if people are looking at it. And if it's not a constant topic of conversation, um, it, it, it won't help at all. And I know, uh, Abi Noda and I have a difference of opinion on how, on data collection. You know, I'm big on, I want real-time data because I'm trying to improve quickly. Uh, he's big on surveys, but for me, and I don't get feedback fast enough on, um, with a survey to be able to correct the course correctly if I'm trying to do, if I'm trying to improve CI and CD. It's good for other stuff. Good for culture. So that's the difference. Um, but make sure that you're not just going out and buying a tool to measure these things that shows data in a way or has, you know, that causes bad behavior, um, or shows, or collects data in a way where it's not collecting it correctly. Really understand what you're doing before you go and implement a tool.
Kovid Batra: Cool. Thanks for that piece of advice, Bryan, Rich. Uh, with that, I think that's our time. Just a quick announcement about the next webinar session, which is with the pioneer of CD, the co-author of the book 'Continuous Delivery', Dave Farley. That will be on 25th of September. So audience, stay tuned. I'll be sharing the link with you guys, sending you emails. Thank you so much. That's it for today.
Bryan Finster: Thanks so much.
Richard Pangborn: I appreciate it.
Kovid Batra: Thanks, Rich. Thanks, Bryan.
Software engineering teams are important assets for the organization. They build high-quality products, gather and analyze requirements, design system architecture and components, and write clean, efficient code. Measuring their success and identifying the potential challenges they may be facing is important. However, this isn’t always easy and takes a lot of time.
And that’s how Engineering Analytics Tools comes to the rescue. One of the popular tools is Jellyfish which is widely used by engineering leaders and CTOs across the globe.
While this is usually the best choice for the organizations, there might be chances that it doesn’t work for you. Worry not! We’ve curated the top 6 Jellyfish alternatives that you can consider when choosing an engineering analytics tool for your company.
Jellyfish is a popular engineering management platform that offers real-time visibility into engineering organization and team progress. It translates tech data into information that the business side can understand and offers multiple perspectives on resource allocation. It also shows the status of every pull request and commits on the team. Jellyfish can be integrated with third-party tools such as Bitbucket, Github, Gitlab, JIRA, and other popular HR, Calendar, and Roadmap tools.
However, its UI can be tricky initially and has a steep learning curve due to the vast amount of data it provides, which can be overwhelming for new users.
Typo is another Jellyfish alternative that maximizes the business value of software delivery by offering features that improve SDLC visibility, developer insights, and workflow automation. It provides comprehensive insights into the deployment process through key DORA and other engineering metrics and offers engineering benchmarks to compare the team’s results across industries. Its automated code tool helps development teams identify code issues and auto-fix them before merging to master. It captures a 360-degree view of developers’ experience and includes an effective sprint analysis that tracks and analyzes the team’s progress. Typo can be integrated with tech tools such as GitHub, GitLab, Jira, Linear, and Jenkins.
LinearB is another leading software engineering intelligence platform that provides insights for identifying bottlenecks and streamlining software development workflow. It highlights automatable tasks to save time and enhance developer productivity. It also tracks DORA metrics and collects data from other tools to provide a holistic view of performance. Its project delivery tracker reflects project delivery status updates using planning accuracy and delivery reports. LinearB can be integrated with third-party applications such as Jira, Slack, and Shortcut.
Waydev is a software development analytics platform that provides actionable insights on metrics related to bug fixes, velocity, and more. It uses the agile method for tracking output during the development process and allows engineering leaders to see data from different perspectives. It emphasizes market-based metrics and ROI, unlike other platforms. Its resource planning assistance feature allows for avoiding scope creep and offers an understanding of the cost and progress of deliverables and key initiatives. Waydev can be integrated with well-known tools such as Gitlab, Github, CircleCI, and AzureOPS.
Pluralsight Flow is a popular tool that tracks DORA metrics and helps to benchmark DevOps practices. It aggregates GIT data into comprehensive insights and offers a bird-eye view of what’s happening in development teams. Its sprint feature helps to make better plans and dive into the team’s accomplished work and whether they are committed or unplanned. Its team-level ticket filters, GIT tags, and other lightweight signals streamline pulling data from different sources. Pluralsight Flow can be integrated with manual and automated testing tools such as Azure DevOps, and GitLab.
Code Climate Velocity is a popular tool that uses repos to synthesize data and offers visibility into code coverage, coding practices, and security risks. It tracks issues in real time to help quickly move through existing workflows and allow engineering leaders to compile data on dev velocity and code quality. It has JIRA and GIT support that compresses into real-time analytics. Its customized dashboard and trends provide a view into each individual’s day-to-day tasks to long progress. Code Climate Velocity also provides technical debt assessment and style check in every pull request.
Swarmia is another well-known engineering effectiveness platform that provides quantitative insights into the software development pipeline. It offers visibility into three key areas: Business outcomes, developer productivity, and developer experience. It allows engineering leaders to create flexible and audit-ready software cost capitalization reports. It also identifies and fixes common teamwork antipatterns such as siloing and too much work in progress. Swarmia can be integrated with popular tools such as Slack, JIRA, Gitlab, Azure DevOps, and more.
While we have shared top software development analytics tools, don’t forget to conduct thorough research before selecting for your engineering team. Check whether it aligns well with your requirements, facilitates team collaboration and continuous improvement, integrates seamlessly with your existing and upcoming tools, and so on.
All the best!
Cycle time is a critical metric that assesses the efficiency of your development process and captures the total time taken from the first commit to when the PR is merged or closed.
PR Review Time is the third stage i.e. the time taken from the Pull Request creation until it gets merged or closed. Efficiently reducing PR Review is crucial for optimizing the development workflow.
In this blog post, we'll explore strategies to effectively manage and reduce review time to boost your team's productivity and success.
Cycle time is a crucial metric that measures the average time PR spends in all stages of the development pipeline. These stages are:
A shorter cycle time indicates an optimized process and highly efficient teams. This can be correlated with higher stability and enables the team to identify bottlenecks and quickly respond to issues with change.
The PR Review Time encompasses the time taken for peer review and feedback on the pull request. It is a critical component of PR Cycle Time that represents the duration of a Pull Request (PR) spent in the review stage before it is approved and merged. Review time is essential for understanding the efficiency of the code review process within a development team.
Conducting code reviews as frequently as possible is crucial for a team that strives for ongoing improvement. Ideally, code should be reviewed in near real-time, with a maximum time frame of 2 days for completion.
If your review time is high, the platform will display the review time as red -
Long reviews can be identifed in the "Pull Request" tab and see all the open PRs.
You can also identify all the PRs having a high cycle time by clicking on view PRs in the cycle time card.
See all the pending reviews in the “Pull Request” and identify them with the oldest review in sequence.
It's common for teams to experience communication breakdowns, even the most proficient ones. To address this issue, we suggest utilizing typo's Slack alerts to monitor requests that are left hanging. This feature allows channels to receive notifications only after a specific time period (12 hrs as default) has passed, which can be customized to your preference.
Another helpful practice is assigning a reviewer to work alongside developers, particularly those new to the team. Additionally, we encourage the team to utilize personal Slack alerts, which will directly notify them when they are assigned to review a code.
When a team is swamped with work, extensive pull requests may also be left unattended if reviewing them requires significant time. To avoid this issue, it's recommended to break down tasks into shorter and faster iterations. This approach not only reduces cycle time but also helps to accelerate the pickup time for code reviews.
When a bug is discovered that requires a patch to be made, a high-priority feature comes down from the CEO. In such situations, countless unexpected events may demand immediate attention, causing other ongoing work, including code reviews, to take a back seat.
Code reviews are frequently deprioritized in favor of other tasks, such as creating pull requests with your own changes. This behavior is often a result of engineers misunderstanding how reviews fit into the broader software development lifecycle (SDLC). However, it's important to recognize that code waiting for review is essentially at the finish line, ready to be incorporated and provide value. Every hour that a review is delayed means one less hour of improvement that the new code could bring to the application.
Certain teams restrict the number of individuals who can conduct PR reviews, typically reserving this task for senior members. While this approach is well-intentioned and ensures that only top-tier code is released into production, it can create significant bottlenecks, with review requests accumulating on the desks of just one or a few people. This ultimately results in slower cycle times, even if it improves code quality.
Here are some steps on how you can monitor and reduce your review time
With typo, you can set the goal to keep the review time under 24 hrs recommended by us. After setting the goal, the system sends personal Slack real-time alerts when PRs are assigned to be reviewed.
Prioritize the critical functionalities and high-risk areas of the software during the review, as they are more likely to have significant issues. This can help you focus on the most critical items first and reduce review time.
Conduct code reviews frequently to catch and fix issues early on in the development cycle. This ensures that issues are identified and resolved quickly, rather than waiting until the end of the development cycle.
Establish coding standards and guidelines to ensure consistency in the codebase, which can help to identify potential issues more efficiently. Keep a close tab on the following metrics that can impact your review time -
Ensure that there is clear communication among the development team and stakeholders to quickly identify issues and resolve them timely.
Peer reviews can help catch issues that may have been missed during individual code reviews. By having team members review each other's code, you can ensure that all issues are caught and resolved quickly.
Minimizing PR review time is crucial for enhancing the team's overall productivity and efficient development workflow. By implementing these, organizations can significantly reduce cycle times and enable faster delivery of high-quality code. Prioritizing these practices will lead to continuous improvement and greater success in software development process.
In the world of software development, high performing teams are crucial for success. DORA (DevOps Research and Assessment) metrics provide a powerful framework to measure the performance of your DevOps team and identify areas for improvement. By focusing on these metrics, you can propel your team towards elite status.
DORA metrics are a set of four key metrics that measure the efficiency and effectiveness of your software delivery process:
DORA metrics provide valuable insights into the health of your DevOps practices. By tracking these metrics over time, you can identify bottlenecks in your delivery process and implement targeted improvements. Research by DORA has shown that high-performing teams (elite teams) consistently outperform low-performing teams in all four metrics. Here's a quick comparison:
These statistics highlight the significant performance advantage that elite teams enjoy. By striving to achieve elite performance in your DORA metrics, you can unlock faster deployments, fewer errors, and quicker recovery times from incidents.
Here are some key strategies to achieve elite levels of DORA metrics:
By implementing these strategies and focusing on continuous improvement, your DevOps team can achieve elite levels of DORA metrics and unlock significant performance gains. Remember, becoming an elite team is a journey, not a destination. By consistently working towards improvement, you can empower your team to deliver high-quality software faster and more reliably.
In addition to the above strategies, here are some additional tips for achieving elite DORA metrics:
By following these tips and focusing on continuous improvement, you can help your DevOps team reach new heights of performance.
As you embark on your journey to DevOps excellence, consider the potential of Large Language Models (LLMs) to amplify your team's capabilities. These advanced AI models can significantly contribute to achieving elite DORA metrics.
By strategically integrating LLMs into your DevOps practices, you can enhance collaboration, improve decision-making, and accelerate software delivery. Remember, while LLMs offer significant potential, human expertise and oversight remain crucial for ensuring accuracy and reliability.
Cycle time is a critical metric for assessing the efficiency of your development process that captures the total time taken from the start to the completion of a task.
Coding time is the first stage i.e. the duration from the initial commit to the pull request submission. Efficiently managing and reducing coding time is crucial for maintaining swift development cycles and ensuring timely project deliveries.
Focusing on minimizing coding time can enhance their workflow efficiency, accelerate feedback loops, and ultimately deliver high-quality code more rapidly. In this blog post, we'll explore strategies to effectively manage and reduce coding time to boost your team's productivity and success.
Cycle time measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process.
Longer cycle time leads to delayed project deliveries and hinders overall development efficiency. On the other hand, Short cycle time enables faster feedback, quicker adjustments, and more efficient development, leading to accelerated project deliveries and improved productivity.
Measuring cycle time provides valuable insights into the efficiency of a software engineering team's development process. Below are some of how measuring cycle time can be used to improve engineering team efficiency:
Coding time is the time it takes from the first commit to a branch to the eventual submission of a pull request. It is a crucial part of the development process where developers write and refine their code based on the project requirements. High coding time can lead to prolonged development cycles, affecting delivery timelines. Managing the coding time efficiently is essential to ensure the code completion is done on time with quicker feedback loops and a frictionless development process.
To achieve continuous improvement, it is essential to divide the work into smaller, more manageable portions. Our research indicates that on average, teams require 3-4 days to complete a coding task, whereas high-performing teams can complete the same task within a single day.
In the Typo platform, If your coding time is high, your main dashboard will display the coding time as red.
Benchmarking coding time helps teams identify areas where developers may be spending excessive time, allowing for targeted improvements in development processes and workflows. It also enables better resource allocation and project planning, leading to increased productivity and efficiency.
Identify the delay in the “Insights” section at the team level & sort the teams by the cycle time.
Click on the team to deep dive into the cycle time breakdown of each team & see the delays in the coding time.
There are broadly three main causes of high coding time
Frequently, a lengthy coding time can suggest that the tasks or assignments are not being divided into more manageable segments. It would be advisable to investigate repositories that exhibit extended coding times for a considerable number of code changes. In instances where the size of a PR is substantial, collaborating with your team to split assignments into smaller, more easily accomplishable tasks would be a wise course of action.
“Commit small, commit often”
While working on an issue, you may encounter situations where seemingly straightforward tasks unexpectedly grow in scope. This may arise due to the discovery of edge cases, unclear instructions, or new tasks added after the assignment. In such cases, it is advisable to seek clarification from the product team, even if it may take longer. Doing so will ensure that the task is appropriately scoped, thereby helping you complete it more effectively
There are occasions when a task can prove to be more challenging than initially expected. It could be due to a lack of complete comprehension of the problem, or it could be that several "unknown unknowns" emerged, causing the project to expand beyond its original scope. The unforeseen difficulties will inevitably increase the overall time required to complete the task.
When a developer has too many ongoing projects, they are forced to frequently multitask and switch contexts. This can lead to a reduction in the amount of time they spend working on a particular branch or issue, increasing their coding time metric.
Use the work log to understand the dev’s commits over a timeline to different issues. If a developer makes sporadic contributions to various issues, it may be indicative of frequent context switching during a sprint. To mitigate this issue, it is advisable to balance and rebalance the assignment of issues evenly and encourage the team to avoid multitasking by focusing on one task at a time. This approach can help reduce coding time.
Set goals for the work at risk where the rule of thumb is keeping the PR with less than 100 code changes & refactor size as above 50%.
To achieve the team goal of reducing coding time, real-time Slack alerts can be utilised to notify the team of work at risk when large and heavily revised PRs are published. By using these alerts, it is possible to identify and address issues, story-points, or branches that are too extensive in scope and require breaking down.
To manage workloads and assignments effectively, it is recommended to develop a habit of regularly reviewing the Insights tab, and identifying long PRs on a weekly or even daily basis. Additionally, examining each team member's workload can provide valuable insights. By using this data collaboratively with the team, it becomes possible to allocate resources more effectively and manage workloads more efficiently.
Using a framework, such as React or Angular, can help reduce coding time by providing pre-built components and libraries that can be easily integrated into the application
Reusing code that has already been written can help reduce coding time by eliminating the need to write code from scratch. This can be achieved by using code libraries, modules, and templates.
Rapid prototyping involves creating a quick and simple version of the application to test its functionality and usability. This can help reduce coding time by allowing developers to quickly identify and address any issues with the application.
Agile methodologies, such as Scrum and Kanban, emphasize continuous delivery and feedback, which can help reduce coding time by allowing developers to focus on delivering small, incremental improvements to the application
Pair programming involves two developers working together on the same code at the same time. This can help reduce coding time by allowing developers to collaborate and share ideas, which can lead to faster problem-solving and more efficient coding.
Optimizing coding time, a key component of the overall cycle time enhances development efficiency and accelerates project delivery. By focusing on reducing coding time, software development teams can streamline their workflows and achieve quicker feedback loops. This leads to a more efficient development process and timely project completions. Implementing strategies such as dividing tasks into smaller segments, clarifying requirements, minimizing multitasking, and using effective tools and methodologies can significantly improve both coding time and cycle time.
Software engineering teams are the engine that drives your product forward. They write clean, efficient code, gather and analyze requirements, design system architecture and components, and build high-quality products. And since the tech industry is ever-evolving, it is crucial to understand how well they are performing and what needs to be fixed.
This is where software development analytics tools come in. These tools provide insights into various metrics related to the development workflow, measure progress, and help to make informed decisions.
One such tool is Waydev that is used by development teams across the globe. While this is usually the best choice for the organizations, there might be chances that it doesn’t work for you.
We’ve curated the top 5 Waydev alternatives that you can consider when selecting engineering analytics tools for your company.
Waydev is a leading software development analytics platform that puts more emphasis on market-based metrics. It allows development teams to compare the ROI of specific products to identify which features need improvement or removal. It also gives insights into the cost and progress of deliverables and key initiatives. Waydev can be seamlessly integrated with Github, Gitlab, CircleCI, Azure DevOps, and other popular tools.
However, this analytics tool can be expensive, particularly for smaller teams or startups and may lack certain functionalities, such as detailed insights into pull request statistics or ticket activity.
A few of the best Waydev alternatives are:
Typo is a software engineering analytics platform that offers SDLC visibility, actionable insights, and workflow automation for building high-performing software teams. It tracks essential DORA and other engineering metrics to assess their performance and improve DevOps practices. It allows engineering leaders to analyze sprints with detailed insights on tasks and scope and provides an AI-powered team insights summary. Typo’s built-in automated code analysis helps find real-time issues and hotspots across the code base to merge clean, secure, high-quality code, faster. With its holistic framework to capture developer experience, Typo helps understand how devs are doing and what can be done to improve their productivity. Its pre-built integration in the dev tool stack can highlight developer blockers, predict sprint delays, and measure business impact.
LinearB is another software delivery intelligence platform that provides insights to help engineering teams identify bottlenecks and improve software development workflow. It highlights automatable tasks to save time and resources and enhance developer productivity. It provides real-time alerts to development teams regarding project risks, delays, and dependencies and allows teams to create customized dashboards for tracking various engineering metrics such as cycle time and DORA metrics. LinearB’s project delivery forecast alerts the team to stay on schedule and communicate project delivery status updates. It can also be integrated with third-party applications such as Jira, Slack, Shortcut, and other popular tools.
Jellyfish is an engineering management platform that aligns engineering data with business priorities. provides real-time visibility into engineering work quickly and allows the team members to track key metrics such as PR statuses, code commits, and overall project progress. It can be integrated with various development tools such as GitHub, GitLab, JIRA, and other third-party applications. Jellyfish offers multiple perspectives on resource allocation and helps track investments made during product development. It also generates reports tailored for executives and finance teams, including insights into R&D capitalization and engineering efficiency.
Swarmia is an engineering effectiveness platform that provides visibility into three key areas: business outcome, developer productivity, and developer experience. Its working agreement feature includes 20+ work agreements, allowing teams to adopt and measure best practices from high-performing teams. It tracks healthy engineering measures and provides insights into the development pipeline. Swarmia’s Investment balance gives insights into the purpose of each action and money spent by the company on each category. It can be integrated with tech tools like source code hosting, issue trackers, and chat systems.
Pluralsight Flow, a software development analytics platform, aggregates GIT data into comprehensive insights. It gathers important engineering metrics such as DORA metrics, code commits, and pull requests, all displayed in a centralized dashboard. It can be integrated with manual and automated testing such as Azure DevOps and GitLab. Pluralsight Flow offers a comprehensive view of team health, allowing engineering leaders to proactively diagnose issues. It also sends real-time alerts to keep teams informed about critical changes and updates in their workflows.
Picking the right analytics tool is important for the software engineering team. Check out these essential factors below before you make a purchase:
Consider how the tool can accommodate the team’s growth and evolving needs. It should handle increasing data volumes and support additional users and projects.
Error detection feature must be present in the analytics tool as it helps to improve code maintainability, mean time to recovery, and bug rates.
Developer analytics tools must compile with industry standards and regulations regarding security vulnerabilities. It must provide strong control over open-source software and indicate the introduction of malicious code.
These analytics tools must have user-friendly dashboards and an intuitive interface. They should be easy to navigate, configure, and customize according to your team’s preferences.
Software development analytics tools must be seamlessly integrated with your tech tools stack such as CI/CD pipeline, version control system, issue tracking tools, etc.
Given above are a few Waydev competitors. Conduct thorough research before selecting the analytics tool for your engineering team. Check whether it aligns well with your requirements. It must enhance team performance, improve code quality and reduce technical debt, drive continuous improvement in your software delivery and development process, integrate seamlessly with third-party tools, and more.
All the best!
As an engineering leader, showcasing your team’s efficiency and alignment with business goals can be challenging. DevOps metrics and KPIs are essential tools that provide clear insights into your team’s performance and the effectiveness of your DevOps practices.
Tracking the right metrics allows you to measure the DevOps processes’ success, identify areas for improvement, and ensure that your software delivery meets high standards.
In this blog post, let’s delve into key DevOps metrics and KPIs to monitor to optimize your DevOps efforts and enhance organizational performance.
DevOps metrics showcase the performance of the DevOps software development pipeline. These metrics bridge the gap between development and operations and measure and optimize the efficiency of processes and people involved. Tracking DevOps metrics enables DevOps teams to quickly identify and eliminate bottlenecks, streamline workflows, and ensure alignment with business objectives.
DevOps KPIs are specific, strategic metrics to measure progress towards key business goals. They assess how well DevOps practices align with and support organizational objectives. KPIs also provide insight into overall performance and help guide decision-making.
Measuring DevOps metrics and KPIs is beneficial for various reasons:
There are many DevOps metrics available. Focus on the key performance indicators that align with your business needs and requirements.
A few important DevOps metrics and KPIs are:
Deployment Frequency measures how often the code is deployed to production. It considers everything from bug fixes and capability improvements to new features. It monitors the rate of change in software development, highlights potential issues, and is a key indicator of agility and efficiency. High deployment Frequency indicates regular deployments and a streamlined pipeline, allowing teams to deliver features and updates faster.
Lead Time for Changes is a measure of time taken by code changes to move from inception to deployment. It tracks the speed and efficiency of software delivery and provides valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies. Short lead times allow new features and improvements to reach users quickly and enable organizations to test new ideas and features.
This DevOps metric tracks the percentage of newly deployed changes that caused failure or glitches in production. It reflects reliability and efficiency and relates to team capacity, code complexity, and process efficiency, impacting speed and quality. Tracking CFR helps identify bottlenecks, flaws, or vulnerabilities in processes, tools, or infrastructure that can negatively affect the software delivery’s quality, speed, and cost.
Mean Time to Recovery measures the average time a system or application takes to recover from any failure or incident. It highlights the efficiency and effectiveness of an organization’s incident response and resolution procedures. Reduced MTTR minimizes system downtime, faster recovery from incidents, and identifies and addresses potential issues quickly.
Cycle Time metric measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process. Measuring cycle time can provide valuable insights into the efficiency and effectiveness of an engineering team's development process. These insights can help assess how quickly the team can turn around tasks and features, identify trends and failures, and forecast how long future tasks will take.
Mean Time to Detection is a key performance indicator that tracks how long the DevOps team takes to identify issues or incidents. High time to detect results in bottlenecks that may interrupt the entire workflow. On the other hand, shorter MTTD indicates issues are identified rapidly, improving incident management strategies and enhancing overall service quality.
Defect Escape Rate tracks how many issues slipped through the testing phase. It monitors how often defects are uncovered in the pre-production vs. production phase. It highlights the effectiveness of the testing and quality assurance process and guides improvements to improve software quality. Reduced Defect Escape Rate helps maintain customer trust and satisfaction by decreasing the bugs encountered in live environments.
Code coverage measures the percentage of a codebase tested by automated tests. It helps ensure that the tests cover a significant portion of the code, and identifies untested parts and potential bugs. It assists in meeting industry standards and compliance requirements by ensuring comprehensive test coverage and provides a safety net for the DevOps team when refactoring or updating code. Hence, they can quickly catch and address any issues introduced by changes to the codebase.
Work in Progress represents the percentage breakdown of Issue tickets or Story points in the selected sprint according to their current workflow status. It monitors and manages workflow within DevOps teams. It visualizes their workload, assesses performance, and identifies bottlenecks in the dev process. Work in Progress enables how much work the team handles at a given time and prevents them from being overwhelmed.
Unplanned work tracks any unexpected interruptions or tasks that arise and prevents engineering teams from completing their scheduled work. It helps DevOps teams understand the impact of unplanned work on their productivity and overall workflow and assists in prioritizing tasks based on urgency and value.
PR Size tracks the average number of lines of code added and deleted across all merged pull requests (PRs) within a specified time period. Measuring PR size provides valuable insights into the development process, helps development teams identify bottlenecks, and streamline workflows. Breaking down work into smaller PRs encourages collaboration and knowledge sharing among the DevOps team.
Error Rates measure the number of errors encountered in the platform. It identifies the stability, reliability, and user experience of the platform. Monitoring error rates help ensure that applications meet quality standards and function as intended otherwise it can lead to user frustration and dissatisfaction.
Deployment time measures how long it takes to deploy a release into a testing, development, or production environment. It allows teams to see where they can improve deployment and delivery methods. It enables the development team to identify bottlenecks in the deployment workflow, optimize deployment steps to improve speed and reliability, and achieve consistent deployment times.
Uptime measures the percentage of time a system, service, or device remains operational and available for use. A high uptime percentage indicates a stable and robust system. Constant uptime tracking maintains user trust and satisfaction and helps organizations identify and address issues quickly that may lead to downtime.
Typo is one of the effective DevOps tools that offer SDLC visibility, developer insights, and workflow automation to deliver high-quality software to end-users. It can seamlessly integrate into tech tool stacks such as GIT versioning, issue tracker, and CI/CD tools. It also offers comprehensive insights into the deployment process through key metrics such as change failure rate, PR size, code coverage, and deployment frequency. Its automated code review tool helps identify issues in the code and auto-fixes them before you merge to master.
DevOps metrics are vital for optimizing DevOps performance, making data-driven decisions, and aligning with business goals. Measuring the right key indicators can gain insights into your team’s efficiency and effectiveness. Choose the metrics that best suit the organization’s needs, and use them to drive continuous improvement and achieve your DevOps objectives.
Typo recently hosted an engaging live webinar titled “The Hows and Whats of DORA,” featuring DORA expert Nathen Harvey and special guest Ido Shveki. With over 170 attendees, we explored DORA and other crucial engineering metrics in depth.
Nathen, the DORA Lead & Developer Advocate at Google Cloud, and Ido, the VP of R&D at BeeHero, one of our valued customers, brought their unique insights to the discussion.
The session explored why only 5-10% of engineering teams actively use DORA metrics and examined the current state of data-driven metrics like DORA and SPACE. It also highlighted the organizational and cultural elements essential for successfully implementing these metrics.
Further, Nathen explained how advanced frameworks such as DORA become critical based on team size and DevOps maturity, and offered practical guidance on choosing the most relevant metrics and benchmarks for the organization.
The event concluded with an engaging Q&A session, allowing attendees to ask questions and gain valuable insights.
P.S.: Our next live webinar is on August 28, featuring DORA expert Bryan Finster. We hope to see you there!
Want to Implement DORA Metrics for Improving Dev Visibility and Performance?
Kovid Batra: All right. Hi, everyone. Thanks for joining in for our DORA Exclusive webinar- The Hows & Whats of DORA, powered by Typo. This is Kovid, founding member at Typo and your host for today's webinar. And with me, I have two special co-hosts. Please welcome the DORA expert tonight, Nathen Harvey. He's the Lead and Dev Advocate at Google Cloud. And we have with us one of our product mentors, Typo Advocates, Ido Shveki, who is VP of R&D at BeeHero. Thanks, Nathen. Thanks, Ido, for joining in.
Nathen Harvey: Oh, thanks for having us. I'm really excited to be here today. Thanks, Kovid.
Ido Shveki: Me too. Thanks, Kovid.
Kovid Batra: Guys, um, honestly, like before we get started, uh, I have to share this with the, with our audience today. Uh, both of you have been really nice. It was just one message and you were so positive in the first response itself to join this event. And honestly, uh, I feel that this, these kinds of events are really helpful for the engineering community because we are picking up a topic which is growing, people want to learn more, and, uh, Nathen, Ido,, once again, thanks a lot for, for joining in on this event.
Nathen Harvey: Oh yeah, it really is my pleasure and I totally agree that these events are so important. Um, I often say that, you know, you can't improve alone. Uh, and that's true that each individual, we can't improve our entire organization or even our entire team on our own. It requires the entire team, but even an entire team within one organization, there's so much that we can learn from each other when we look into other organizations around the world and other challenges that people are running into, how they've overcome them. And I truly believe that each and every one of us has something to share with the community, uh, even if you were just getting started, uh, maybe you found a new pitfall that others should avoid. Uh, so you can bring along those cautionary tales and share those with, with the global community. I think it's so important that we continue to learn from and, and be inspired by one another.
Kovid Batra: Totally. I totally agree with that. All right. So I think, we'll just get started with you, Nathen. Uh, so I think the first thing that I want to talk about is very fundamental to the implementation of DORA, right? We know lately we had a Gartner report saying there were only 5 to 10 percent of teams who actually implement such frameworks through tools or through processes in their, in their organizations. Whereas, I mean, I have grown up in my professional career hearing that if we are measuring something, only then we can improve it. So if you go to any department or any, uh, business unit for that matter, everyone follows some sophisticated processes or tooling to measure those KPIs, right? Uh, why is it, why this number is so low in our engineering teams? And if let's say, they are following something only through What's the current landscape according to you? I mean, you have been such a great believer of all this data-driven DORA metrics, engineering metrics, SPACE. So what's, what's your thought around it?
Nathen Harvey: Yeah, it's a, it's a good question. And I think it's really interesting to think about. I think when you look at the practice of software engineering or development, or even operations like reliability engineering and things along those lines, these all tend to be, um, one creative work, right? When you're writing software, you're probably writing things that have never been written before. You're trying to solve a new problem that's very specific to your context. Um, it can be very difficult to measure, what does that look like? I mean, we've, we've used hundreds of different measures over the years. Some are terrible. You know, I think back to a while ago, and hopefully no one watching is under this measurement today. But how many lines of code did you commit to the repository? That's, that's a measure that has certainly been used in the past to figure out, is this a develop, is this developer being productive or not? Uh, we all know, hopefully by now that that's a, it's a terrible way to measure whether or not you're delivering value, whether or not you're actually being productive. So, I think that that's, that's part of it.
I also think, frankly, that, uh, until a few years ago, the world was working in a, in a way in which finances were easy to get. We were kind of living in this zero interest rate, uh, world. Um, and engineers, you know, we're, we're special. We do work that is, that can't be understood by anyone else because we have this depth of knowledge in exactly what we're doing. That's kind of a lie. Uh, those salespeople, those marketing people, they have a depth of knowledge that we don't understand, that we couldn't do their job in the same way that they couldn't do our job. And that's, that's not to say that one is better than the other, or one is more special than the other, but we absolutely need different ways to measure. And even ways that we have to measure other sort of disciplines, uh, don't actually give us the whole picture. Take sales, for example, right? You might look at well, uh, how much, uh, how much revenue is this particular salesperson bringing in to the organization? That is certainly one measure of the productivity of that salesperson, but it doesn't really give you the whole picture, right? How is that salesperson's experience? How are the people that are interacting with that salesperson? How is their experience? So I think that it is really difficult to agree on a good set of measures to understand what those measures are. And frankly, and this, this might be a little bit shocking, Kovid, but look, I, I, I am a big proponent of DORA and the research and everything that we've done here. But between you and me, I don't want you to do DORA metrics. I don't want you to. I don't care about the DORA metrics. What I care about is that you and your team are improving, improving the practices and the processes that you have to deliver and operate software, improving the well-being of the members of your team, improving the value that you're creating for your business, and improving the experience that you're creating for your customers.
Now, none of those are the DORA metrics. Of course, Measuring the DORA metrics helps us assess some of those things and what we've been able to show through the research is that improving things like software delivery performance have positive outcomes or positive predictive nature of better organizational success, better customer satisfaction, better well-being for your teams. And so, I think there's there's this point where, you know, there's, uh, maybe this challenge, right, do you want, do you want me to spend as an engineer? Do you want me to spend time measuring the work that I'm doing, measuring how much value am I delivering, or do you want me delivering more value? Right? And it's not really an either or trade-off, but this is kind of some of the mindsets I have. And I think that this is some of the, the blockers that come in place when people want to try to bring in a measurement framework or a metrics framework. And then finally, Uh, you know, between you and me, nobody really likes their work to be measured. I want to feel like I'm providing valuable work and, and know that that's the case, but if you ask me to measure it, I start to get really worried about why are you asking that question. Are you asking that question because you want to give me a raise and a promotion and more money? Great. I'm gonna make sure that these numbers look really good. If you're asking that question to figure out if you need to keep me on board, or maybe you can let me go, now I'm getting really nervous about the questions that you're asking.
And so I think there's a lot of like human nature in the prevention of adopting these sorts of frameworks. And it really gets back to, like, who are these frameworks for? And again, I'll just go back to what I said sort of towards the beginning. I don't want you to do DORA metrics. I want you to improve. I want you to get better. And so, if we think about it in that perspective, really the DORA metrics are for me and my teammates. They aren't necessarily for my leaders. Because it's me and my teammates that are going to make those improvement efforts.
Kovid Batra: Totally. I think, um, very wise words there. One thing that I just picked up from what you just said, uh, from the narrative, like there is a huge organizational cultural play in this, right? People are at the center of how things get implemented. So, you have been experiencing this with a lot of teams. You have implemented this. What's the difference that you have seen? What are those mindsets which make these things implement actually? What are those organizational factors that make these things implement?
Nathen Harvey: Yeah, that's a, that's a good question. I would say, first it starts with, uh, the team that you're going to start measuring, or the application, the group of people and the technology that you want to start measuring. First, these people have to want to change, because if we're, if we're going to make a measure on something, presumably we're making that measure so that we understand how we are, so that we can improve. And to improve, we have to change something. So it starts with the people wanting to change. Oh, except I have to be honest, that's not enough. Wanting to change actually isn't enough. We all want to change. We all want to get better. Actually, maybe we all just want to get better, but we don't want to have to change anything. Like I'm very comfortable in the way that I work. So can it, can it just produce better results? The truth is, I think we have to find teams that need to change. There has to be some. Motivating factor that's really pushing them to change because after we look at the dashboard, after we see some numbers, if we're not truly motivated, if there isn't a need for us to change, we're probably not going to change our behavior. So I think that's the first critical component is this need to improve, this fundamental desire that goes beyond just the desire. It's, it's a motivating factor. You have to do this. You have to get better because the competition is coming after you, because you're feeling burnt out, because for a myriad of reasons. So I think that that's a big first step in it.
Kovid Batra: A lot of times, what I have seen while talking to a lot of my Typo clients also, uh, is, uh, they feel that there is a stage when this needs to be implemented, right? So people use Git metrics, Jira metrics to make sure things are running fine. And I kind of agree to them, like very small teams can, can rely on that. Like maybe under 10 size teams are good. But, what do you think, uh, what, what's the DevOps maturity? What's the team size that impacts this, where you need to get into a sophisticated framework or a process like DORA to make sure things are, uh, in, in the right visibility?
Nathen Harvey: Yeah, that's, that's, that's a really good question. And I think unfortunately, of course, the answer is it, it depends, right? It is pretty context-specific. I do think it matters that, uh, it matters the level at which you're measuring these things. You know, the DORA metrics have always been meant, and if you look at our survey, we always sort of prepend our questions with, for the primary application or service that you're working on. So when we think about those DORA metrics, those software delivery metrics in particular, we aren't talking about an organization. What is the, you know, we don't ask, for example, what is the deployment frequency at Typo? But instead, we ask about specific applications within Typo, and we expect that you're going to have variation across the applications within your team. And so, when you have to get into this sort of more formal measurement program, I think that really is context-specific. It really depends on the business and even what are you measuring? In fact, if if your team has, uh, more of a challenge with developing code than they do with shipping code, then maybe the DORA metrics aren't the right metrics to start with. You want to sort of find your constraint within your organization, and DORA is very much focused on software delivery and operational performance. So on the software delivery piece, it's really about are we able to take this code that was written and get it out the door, put it in front of customers. Of course, there's a lot of things on the development side that enable that. There's a lot of things on the operational side that benefit from that. It all kind of comes together, but it is really looking at finding that particular pain point or friction point within your organization.
And then, I think one other thing that I'll just comment on really quickly here is that as teams start to adopt these frameworks, there's often an overfitting for precision. We need precise data when it comes to this. And honestly, again, if you go back to the methods that DORA uses, each year we run an annual survey. We ask people, what is your average time or your typical time from code committed to code in production? We're not hooking into your Git systems or your software delivery pipelines or your, uh, task backlog management systems. We're not hooking into any of those things. We're asking about your experience. Now, we have to do that given that we're asking the entire world. We can't simply integrate with all of those systems. But this level of precision is very helpful at some point. But it doesn't necessarily need to be where you start. Right? Um, I always find it's best to start with a conversation. Kind of like what we're having today.
Kovid Batra: But yeah, I think, uh, the toolings that are coming into the, into the picture now are solving that piece also. So I think both the things are getting, uh, balanced there because I feel the survey part is also very critical to really understand what's going on. And on top of that, you have some data coming from the systems without any effort that reduces your pain and trust on what you are looking at. So yeah, that makes sense.
Nathen Harvey: Yeah, absolutely. And, and, and there is a cautionary tale built in there. I've seen, I've seen too many teams go off and try to integrate all of these systems together to get all of the precise data and beautiful dashboards. Sometimes that effort ends up taking months. Sometimes that effort ends up taking years. But what those teams fail to do over those months or years is actually try to improve anything. All they're trying to improve is the precision of the data that they have. And so, at the end of that process, they have more precise, a more precise understanding of what they knew at the beginning of that process.
And they haven't made any improvements. So that's where a tool like Typo, uh, or others of this nature like really come in because now I don't have to think about as much, all of that integration, I can, I can take something off the shelf, uh, and run it in my systems and immediately start to get value from that.
Kovid Batra: Totally. I think, uh, when it comes to using the product, uh, Ido has been, uh, one of the people who has connected with me almost thrice in the last few days, giving me some feedback around how to do things. And I would let Ido have some of his, uh, questions here. And, uh, I have, uh, my demo dashboard also ready. So if there is anything that you want to refer back to Ido or Nathen, to like highlight some metrics that they can look at, I, I'll be happy to share my screen also. Uh, over to you, Ido. I'll, I'll put you on the main screen so that the audience sees you well.
Ido Shveki: Oh, thanks, Kovid. And hi again, Nathen. Uh, first of all, very interesting, uh, and you speaking about it. I also find this topic, uh, close to my heart. So I, uh, I, it's a fascinating to hear you talk about it. I wanted to know, uh, if you have any, you mentioned before that among the different, like you said, it may be inside the Typo as a company, there are like different benchmarks, different, uh, so how can you identify this, uh, benchmark? Maybe my questions are a bit practical, but let me know if that's the case, but yeah, I just want to know how to identify this benchmark because as you mentioned, and also at BeeHero, we have like, uh, uh, different teams, different sizes, different maturity, uh, different, uh, I mean, uh, seniority level. So how can I start with these benchmarks?
Nathen Harvey: Yeah, yeah. That's a, that's a really great question. So, um, one of the things that I like to do when I get together with a new team is we first kind of, or a new organization first, let's, let's pick an application or two. So at, BeeHero, uh, I, I, I know very little about what BeeHero does, you know, tell us a little bit about BeeHero. Give us, give us like a 30-second pitch on BeeHero. What do you do there?
Ido Shveki: Cool. So we are an Israeli startup where we deal with agriculture. What we do is we place, uh, sensors inside beehives as the, as the name might, uh, you know, give you a hint. Uh, we put sensors inside beehives and this way we can give a lot of, uh, we, we collect metrics and we give great, uh, like, uh, good insights, interesting insights to beekeepers, uh, so that they can know what to do with their bee colony, how to treat it, and how to maintain the bee colony. So, this is, you know, basically, and if, if I'm, uh, to your question, so we have, yeah, uh, different platforms. We have the infra platforms, we have the firmware guys, we have mobile app, et cetera. So. But I assume that like every company has this, different angles of a product.
Nathen Harvey: Yeah. Yeah. Yeah. Of course. Every company has hundreds, maybe thousands of different products that they're maintaining. Yeah, for sure. Um, not, well that's first, that's super cool. Um, keeping the farmers and the bees happy. Now, so what I like to do with, with a new team or organization that I'm working with is we start with an application on our service. So maybe, maybe we take the mobile application that BeeHero has and what we want to do is bring together, in the perfect world, we bring together into a physical room, everyone that's responsible for prioritizing work for that application, designing that work, writing the software, shipping the software, running the service, answering customer requests, all of that stuff. Uh, perhaps we'd let the bees stay in the hives. We don't bring them into the room with us. Um, software engineers aren't, aren't known for being good with bees, I guess. So, but..
Ido Shveki: They do affect the metrics though. Yeah, I don't want, I don't want that.
Nathen Harvey: Absolutely. Absolutely. So, so we'll bring these people together. And I like to just start with a conversation, uh, at dora.dev, we have a quick check, that allows you to quickly answer those for software development or software delivery performance metrics. You know, the deployment frequency, change lead time, your change failure rate and your failed deployment recovery time. But even before we get to those metrics, I like to start with a simpler question. Okay, so together as a team, a developer has just committed a change to the version control system. As a team, let's go to the board and let's map out every step in the process, every handoff that has to happen between that code commit and that code landing in production, right, so that the users can use it. And the reason we bring together a cross-functional team is because in many organizations, I don't know how big BeeHero is, but in many organizations, there are handoffs that happen from one team to the next, sort of that chain of custody, if you will, to get to production. Unfortunately, every single one of those handoffs is an opportunity for introducing friction, for hiding information, you know. I've, I've worked with teams as an example where the development team is responsible for building a package, testing that package and then they hand it off to the test team. Well, the test team does, takes that package and they discard it. They go back to the Git repo. They actually clone the Git repo and then they build another package and then start testing that. So now, the developers have built a package that gets discarded. Now the testers build another package that they test against that probably gets discarded and then someone else builds a third package for production. So there's, as you can imagine, there's lots of ways for that handoff and those three different packages to be different from one another. This is, it's, it's mind boggling. But until we put all those people in the room together, you might not even see that friction and that waste in the process. So I start there to really identify where are those friction points? Where are those pain points? And oftentimes you have immediate sort of low hanging fruit, if you will, immediate improvement opportunities.
And the most exhilarating part of that process as a facilitator is to see those aha moments. "Oh my gosh! I didn't realize that you did that." "Oh, I thought I packaged it this way so that you could do this thing that you're not even doing. You're just rubber stamping and passing it on." Or whatever it is. Right? So you find those things, but once you've done that map, then you go back to those four questions. How's my, what are my, you know, we used a quick check in that process. What does my software delivery performance look like? This gives us a baseline. This is how we're doing today. But in this process, we've already started to identify some of those areas for improvement that we want to set next. Now I do this from one team to the next or encourage the teams to do this on their own. And this way we aren't really comparing, you know, what is your mobile app look like versus the front end website, right? Should they have the same deployment frequency? I don't know. They have different customers. They have different needs. They have different teams that are working on them. So you expect them to be different. And the thing that I don't really care about over time is that everyone gets to the top level or a consistent performance across all of the teams. What I'd much rather see is that everyone is improving over time, right? So in other words, I'd rather reward the most improved team than the team that has the highest performance. Does that make sense?
Ido Shveki: Yeah, a lot actually.
Nathen Harvey: All right.
Ido Shveki: Thanks.
Nathen Harvey: Awesome. Yeah.
Ido Shveki: Kovid, do we have another, time for another question?
Kovid Batra: Yeah, I do. I mean, uh, you can go ahead, please. Uh, we have another three minutes. Yeah.
Ido Shveki: Oh, cool. I'll make it quick. I'm actually interested in how do you aligned the business to DORA metrics? Because I usually I find myself talking to the management, CEO, CTO, trying to explain to them what's, what's happening under the hood in the developer team and it's not always that easy. Do you have some tips there?
Nathen Harvey: Yeah, you know, as your CEO come to you and said, You know, you know, last year you did 250 deploys. If you do 500 this year, I'm going to double your salary. They probably never said that to you. Did that?
Ido Shveki: No, no.
Nathen Harvey: No, no. Primarily because your CEO probably doesn't care how many deploys you delivered. Your CEO.
Ido Shveki: And I think that's, I mean, I wouldn't want them to.
Nathen Harvey: You don't want them to. You're, you're exactly right. But they do care about other things, right? They care about, I don't, I don't know, I'm going to make up some metrics. They care about how many, uh, like the health of the hives that each farmer has, right? Like, that's what they care about. They care about how many new farmers have signed up or how many new beekeepers have signed up, what is their experience like with BeeHero. And, and so really, as you go to get your executives and your management and, and the business tied into these metrics, it's probably best not to talk about these metrics, but better to talk in terms of the value that they care about, the measures that they care about. So, you know, our onboarding experience has left some room for improvement. If we ship software faster, we can improve that onboarding experience. And really it's a hypothesis. We believe that by improving our software delivery performance, we'll be able to respond faster to the market needs, and we'll be able to therefore improve our onboarding process as an example, right? And so now you can talk to your CEO or other business counterparts about look, as we've improved these engineering capacities and capabilities, we've seen this direct impact on our customers, on the business value that we care about. DORA shows, through our data collection, that software delivery performance is predictive of better organizational performance.
But it's up to you to prove that, right? It's up to you, essentially, we encourage you to replicate our study. We see this when we look across teams. Do you see this on your team? Do you see that improving? And that's really, I think, how you should talk about it with your business counterparts. And frankly, um, you, you are the business as well. So it also encourages you and the rest of the engineers on your team to remember, we aren't creating this application because we want to use the new, uh, serverless technology, or we want to play with the latest, greatest AI. We're building this application to help with the health of bees, right? And so, keeping that connection back to the business, I think is really important.
Kovid Batra: Okay. On your behalf, can I ask one question?
Yeah. So I think, uh, there are certain things that we also struggle with, with not just Ido, but, uh, various other clients also that, which metrics to pick up. So can we just run through a quick example from your, uh, history of clients where you have, uh, probably highlighted for, uh, let's say, a 100-member dev team. What metrics make sense in what scenario? I, I'll quickly share my screen. Uh, I have some metrics highlighted for, for DORA and more than that on Typo.
Nathen Harvey: Oh, great!
Kovid Batra: You can tell me which metrics one should look at and how one should navigate through it.
Nathen Harvey: Yeah, for sure. That, that'd be awesome. That'd be awesome. So I think as you're pulling up your screen, I'll just start with, you know, the, the reason that the DORA software delivery metrics are nice, is kind of multifold, right? First, there's only four of them. So you can, you can count them on one hand. That's, that's a good thing. Uh, you aren't over, like over, have too many metrics that you just don't know. How many, which lever should we pull? There's too many in front of me, right? Second. Um, they, they represent both lagging and leading indicators. In other words, they're lagging indicators for what does your engineering process look like? What's engineering excellence or delivery excellence look like within your organization? These DORA metrics can tell you. Those are the lagging indicators. You have to change things over here to make them improve. But they're leading indicators for those business KPIs, right? Organizational performance, well-being for the people on your team. So as we improve these, we expect those things to improve as well. And so, the nice thing about starting with those four metrics is that it gives you a good sense of where you are. Gives you a nice baseline.
And so, I'm just going to make my screen a little bit bigger so I can see your, uh, yeah, that's much better. I can see your dashboard now. All right. So you've got, uh, you've got those, uh, looks like those four, uh, a couple of those delivery metrics you got, uh, oh, actually tell me what, what do you have here, Kovid?
Kovid Batra: Yeah. So we have these four DORA metrics for us, the cycle time, deployment frequency, change failure rate, and mean time to restore. So we also believe in the same thing where we start off with these fundamental metrics. And then, um, we, we have more to deep dive into, like, uh, you can see things at team level, so there are different teams in one single view where you can see each team on high level, how their velocity, quality, and throughput looks like. And when you deep dive, you find out those specific metrics that basically contribute to velocity, quality, and throughput of the teams. And these are driven from DORA and various other metrics that we realized were important and critical for people to actually measure what's going on.
Nathen Harvey: Yeah. Yep, that's great. And so I really like that you can see the trend over time because honestly, the, the single number doesn't really mean anything to you. It's like getting on the scale in the morning. There's a number on the scale. I don't know if that's good or bad. It depends on what it was yesterday and what it will be tomorrow. So seeing that trend is the really important thing here because then you can start to make decisions and commitments as a team on experiments that you want to run, right? And so in this particular case, you see your cycle time going up. So now what I want to do is kind of dig in. Well, what's, what's behind the cycle time, what's causing this? And that's where the things like the, that map and, and you see here, we've got a little map that shows you exactly sort of what happens along that flow. So let's take a look at those. We have coding, pick up, review and merge, right? Okay, yup. And so the, nice thing there is that the pickup seems like it's going pretty well, right? One of the things that we found last year in our survey was that teams with faster code reviews have 50 percent better software delivery performance. And so it looks like this team is doing pretty good job. I imagine that pickup is you're reviewing that code, right?
Kovid Batra: Yeah. Yeah. Yeah.
Nathen Harvey: Mm hmm. Yeah. So, so that's good. It's good to see that. But what's the review? Oh, I see. So pickup must be when you first grab the PR and then review maybe incorporates all the sort of back and forth feedback time.
Kovid Batra: Yes, yes. And finally, when you're merging it to your main branch, so the time frame between that is your review time.
Nathen Harvey: Ah, gotcha, gotcha, gotcha. Okay, so for me, this would be a good place to dig in. What's, what's happening there? Because if you look between that pickup and review, that's about 8 hours of your 5, 10, 15, uh, 18 hours. So it's a significant portion there is, sort of in that code review cycle. This is something I'd want to look at.
Kovid Batra: Perfect.
Nathen Harvey: Yeah. Yeah. And we see this, we see this a lot. Um, one, one organization I worked with, um, the, the challenge that they had was not necessarily in code review, but in approvals, they were in a regulated industry and they sent all changes off to a change approval board that had to approve them, that change approval board only met so frequently, as you can imagine, that really slowed down their cycle time. Uh, it also did not help with their stability, right? Um, changes were just as likely to fail when they went to production as not, uh, regardless of whether or not they went through that change approval board. So we really looked at that change approval process and worked to help them automate that. The net result is I think they're deploying about 600 times more frequently today than they were before we started the process, which is pretty incredible.
Kovid Batra: Cool. That's really helpful, Nathen. And thanks for those examples that fit into the context of a lot of our audience here. In fact, this question, I just realized was asked by Benny Doan also. So I think he would be happy to hear you. And, uh, I think now it's time. I feel the audience can ask their questions. So, um, we'll start with a 15 minute Q&A round where all the audience, you are free to comment in the comment sections with all the questions that you have. And, uh, Nathen, Ido, uh, would be happy to listen out to you on those particular questions.
Ido Shveki: Kovid, should we just start answering these questions?
Kovid Batra: Yeah.
Nathen Harvey: Yeah, I'm having trouble switching to the comments tab. So maybe you could read some of the questions. I can't see them.
Ido Shveki: Um, I can see a question that was also asked by Benny, which I worked in the past. Oh, hi, Benny. Nice that you're here. Um, about how to, like, uh it was by Nitish and Benny as well, asking about how does the, the dev people, the developers won't feel micromanaged when we are using, um, uh, the DORA metrics with them. Um, I can begin, I'll let you Nathen, uh, uh, elaborate on it in a second. I can begin with my experience thing that first of all, it is a slippery slope. I mean, I do find it not trivial to, to, um, like if you would just show them that I'm looking at the times from this PR to the improvement and line of codes, et cetera, like Nathan said in the beginning. Yeah. I mean, they would feel micromanage. Um, I, uh, first of all, I, I usually talk about it on a, on a team level or an organization level. And when I do want to raise this, uh, questions or maybe like address them as a growth opportunities for a certain developer. Uh, personally, I don't look at it as a, like criticism. I, it's like a, it's a beginning of a conversation. It's not like I don't know. I didn't make up my mind before. Uh, and because of this metric looks like this, then I'm not pleased with how you perform. It's just like, All right. I've seen that there is a decrease here. Uh, is there a reason? Let's talk about, let's discuss it. I'm easily convinced if there are like, uh, ways to be convinced. And, but, but yeah, I do look at it as a growth. Um, I try to, to, to convince and I do look at it as a, like a, growth opportunity for the developer to, to look at, uh, yeah, that's, that's at least my take over this.
Nathen Harvey: Yeah, I definitely agree with that, you know, because I think that this question really gets to a question of trust. Um, and how do you build trust with your teammates? And I think the way that you build trust is through your actions. Right? And so if you start measuring and then start like taking punitive action against individual developers or even teams, that's going to, your actions are going to tell people, you should be afraid of these metrics. You should do whatever you can to not be measured by these metrics, right? But instead, if and DORA talks a lot about culture, if you lean in and use this as an opportunity to improve, an opportunity to learn more about how the team is going. And, and I like your approach there where you're taking sort of an inquisitive approach. Hey, as an example, you know, Hey, I see that the PRs, uh, that you started to submit fewer PRs than you have in the past, what's going on? It may be that that person has, for the time being, prioritized code reviews. So they're doing less PRs. It may be that they're working on some new architectural thing. They're doing less PRs. It may be that, uh, they've had a family emergency and they've been out of the office more. That's going to lower their PRs. That's the, the, the fact that they have fewer PRs is not enough information for you to go on. It is a good place to start a conversation.
And then, I think the other thing that really helps is that you use these metrics at the team level. So if you as a team start reviewing them, maybe during your regular retrospectives or planning sessions, and then, importantly, it comes back to what are you going to change? Is the team going to try something different based on what they've learned from these metrics? Oh, we see that our lead time is going up, maybe our continuous integration practices, we need to put some more effort into those or some more automated testing. So over the next sprint or time block, we're going to add, you know, 20 percent more capacity for automated testing. And let's see how that impacts things. So seeing that these metrics are being used to inform improvements, that's how you prevent that slippery slope, I think.
Kovid Batra: Totally. Okay. I think we can move on to this next question. Uh, this is Nitish. Uh, how can DORA and data-driven approach be implemented in a way that devs don't feel micromanaged? Yeah, I think.
Nathen Harvey: Yeah, I think, I think we've covered a little bit of this in the previous question here, Nitish. And I think that it really comes back to remembering that these are not measures that should be at the individual level. We're not asking, Kovid, what's your deployment frequency? You know, what's yours? Oh, one of you is better than the other. Something's going to change. No, no, no. That's not how we, that's not how we use these measures. They're really meant for that application or service level. When it comes to developing, delivering, operating software or any technology, that's a team sport. It's not an individual sport.
Kovid Batra: All right. Then we have from Abderrahmane, uh, how are the market segments details used for benchmark collected?
Nathen Harvey: Yeah, this is a really good question. Thanks for that. So, uh, as you know, we run a survey each year and we ask about what industry are you in? Um, and what we found surprisingly, maybe, maybe not surprisingly is that over the years, industry is not really a determinant of how your software delivery performance is going to be. In other words, what we see across every industry, whether it's technology or retail or government or finance, we see teams that have really good software delivery performance. We also see in all of those industries, teams that have route rather poor software, but lots of opportunities to improve their software delivery performance, I should say. Yeah.
So we see that, uh, the market segments are there and, and honestly, we, we publish that data so that people can see that, look, this can happen in our industry too. Um, I always worry that, you know, someone might use their industry as a reason not to question the status quo. Oh, we're in a regulated industry, so we can't do any better. It doesn't matter what industry you're in. You can always do better. You can always do worse as well. So just be careful, like focus on that improvement.
Kovid Batra: Cool. Uh, next question is from Thomas. Uh, how do you plan the ritual with engineers and stakeholders when you're looking at this metric? Yeah, this is a very, uh, important question. I think Nathen, would you like to take this up?
Nathen Harvey: Yeah, I'll take this. I'd love to hear how Ido is doing this as well, sort of incorporating the metrics into their daily work. But I think it's, it's, it's just that as you go into your planning or retrospective cycle, maybe as a team, you think about the last period and you pull up maybe the DORA quick check, or if you're using Typo or something like it, you pull up the dashboard and say, "Look, over the last two weeks over the last month, here's where we're trending. What are we going to do about that? Is there something that we'd like to change about that? What can we learn about that?" Start just with those questions. Start thinking about that. So I think really just using it as a, as a discussion point in those retrospectives, maybe an agenda item in those retrospectives is a really powerful thing that you can do.
Ido, what's your experience?
Ido Shveki: Yeah. So, um, I totally agree. And I think in most parts, this is what I, what I also do, we're also doing at BeeHero, in their perspectives, maybe not on, bi-weekly basis, like every two weeks because sometimes they find it too often and not too much to, to, you know, to, uh, I want it to be, uh, tell them something new, let's say. Um, but also I do find it in what we, when we are doing some rituals for some incident that happened and we're discussing this issue, I really put emphasis, and I think this is the cultural part that you mentioned before. Uh, in this rituals of, uh, incident, uh, rituals. I really try to point out and look at, uh, uh, how long did it take us to mitigate it? Um, when, like how, how long, uh, until the, the customer didn't see the issue. And from these, uh, points, you can, I mean, I hope the team understands the culture that I'm pushing towards. And from that point, they will also want to implement DORA metrics without even knowing the name DORA. We don't really care about the name. I mean, we don't really, it doesn't really matter if they know how to call it. Just like to, as you mentioned before, I don't want you to know about DORA. Just get better or just be better at this. So yeah, that's basically it.
Nathen Harvey: Thanks. Awesome.
Kovid Batra: All right. I think there is one thing that I wanted to ask from this. It's good with the engineers, probably, and you can just pull in every time. But when it comes to other stakeholders in the business, what I have seen and experienced with my clients is they find it hard to explain these DORA metrics in terms of the business language. I think Nathen, you touched upon this in the beginning. I would like to just highlight this again for the audiences' sake.
Nathen Harvey: Yeah, I think that, I think that's really important. And I think that when it comes to dashboards, uh, it, it would be really good to put your delivery performance metrics right next to your organizational performance metrics, right? Are we seeing better customer, like, are we seeing the same trend? As software delivery improves, so do customer signups, so do, uh, revenue that we get per customer or something along those lines. That's, you know, if you think about it, we're really just trying to validate an experiment. We think that by shipping this feature, we're going to improve revenue. Let's test that. Let's look at that side-by-side.
Kovid Batra: Totally. All right. Uh, we have a lot of questions coming in. Uh, so sorry, audience, I'm not able to pick all of those because we are running short on time. We'll pick one last question. Uh, okay. That's from Julia. Uh, are there any variations of DORA metrics you have found in customer deployed or installed software? Example, deployment frequency may not be directly relevant. A very relevant question. So yeah.
Nathen Harvey: Yeah, absolutely. Uh, I think, I think the beauty of the four key metrics is that they are very simple, except they're not, they are very simple on the surface, right? If, and if you take just, let's just take one of them, change lead time. In DORA's language, that starts at like change is committed and it ends when that changes in production. Okay. What does committed mean? Is it committed to a branch? Is it committed to the main line? Is that branch, has that branch been merged into the main line? Who knows? Um, I have a perspective, but it doesn't really matter what my perspective is. When it comes to production. What does it mean to be in production? If we're doing, um, progressive deploys, does it mean the first user in production has it or only when 100 percent of users have it? Is that when it's in production? Or somewhere in between? Or we're running mobile applications where we ship it off to the app store and we have to wait for it to get approved, or installed software where we package it up and we shrink wrap it into a box and we ship out a CD. Is that deployed? I mean, I don't, I don't know that anyone does that any, well, I'm sure it happens. I know in the Navy they put software on helicopters and they fly it out to ships. So that's, you know, all of these things happen. Here's the thing. For your application, what you need to do is think about those four metrics and write down for, for this application, commit, change, change lead time starts here at this event ends here at that event. We're going to write that down probably in maybe something like an architectural decision record and ADR, put it into the code base. And as you write it down, make sure that it's clear, make sure that everyone agrees to it, and probably just as importantly, make sure that when you write it down, you also write down the date at which we will revisit this decision, right? Because it doesn't have to be set in stone. Maybe this is how we're going to measure things starting today, and we'll come back to this in six months. And some of the things that drive that might be the mechanics of how you deliver software. Some of the things that drive that might be the data that you have access to, right? And over time, you may have access to more precise data, additional data that you can then start to use in that. So the important thing is that you take these metrics and you contextualize them for your team. You write down what those metrics are, what their definitions are for your team and you revisit those decisions over time.
Kovid Batra: Perfect. Perfect. All right. I think, uh, it's already time. Nathen, would you like to take one more question?
Nathen Harvey: Uh, I'm happy to take one more question. Yes.
Kovid Batra: All right. All right. So this is going to be the last one. Sorry if someone's question is not being asked here. But let's, let's take this up. Uh, this is Jimmy. Uh, do you ever try to map a change in behavior/automation/process to a change in, in the macro-DORA performance? Or should we have faith that our good practices is what is driving positive DORA trends?
Nathen Harvey: Um, I think that, uh, having faith is a good thing to do. Uh, but validating your experiments is an even better thing to do. So, uh, as an example, uh, let's see, trying to change a behavior automation process to a change in the macro performance. Okay. Uh, I'll, I'll, I'll pick a change that you might have or an automation that you might have. Let's say that, uh, today, your deployment process is a manual process, uh, and you're doing, and there's lots of steps that are manual, uh, and you want to automate that process. Uh, so, we can figure out what are our, what's our software delivery performance look like today, you can use a Typo dashboard, you could use the DORA quick check. Write that number down. Now make some investments in automation, deployment automation, you've deployed, instead of having 50 manual steps, you now have 10 manual steps that you take and 40 that have been automated. Now let's go back and remeasure those DORA performance metrics. Did they improve? One would think and one would have faith that they will have improved. You may find for some reason that they didn't. But, validating an experiment and invalidating an experiment are kind of the same thing. In either case, it's really about the approach that you take next. Are you using this as an opportunity to learn and decide how are we going to respond to the, the new information that we have? It really is about a process of continuous learning, And hopefully continuous improvement, but with every improvement, there may be setbacks along the way.
Kovid Batra: Great. All right. On that note, I think that's our time. We tried to answer all the questions, but of course we couldn't. So we'll have more sessions like this, uh, to help all the audience over here. So thanks a lot. Uh, thank you for being such a great audience. Uh, we hope this session helped you build some great confidence around how to implement DORA metrics in your teams.
And in the end, a heartfelt thanks to my cohosts, Nathen and Ido, and to my Typo team who made this event possible. Thanks a lot, guys. Thank you.
Nathen Harvey: Thank you so much. Bye bye.
Ido Shveki: Thanks for having us. Bye.
Software metrics track how well software projects and teams are performing. These metrics help to evaluate the performance, quality, and efficiency of the software development process and software development teams' productivity. Hence, guiding teams to make data-driven decisions and process improvements.
Process Metrics are quantitative measurements that evaluate the efficiency and effectiveness of processes within an organization. They assess how well processes are performing and identify areas for improvement. A few key metrics are:
Development Velocity is the amount of work completed by a software development team during a specific iteration or sprint. It is typically measured in terms of story points, user stories, or other units of work. It helps in sprint planning and allows teams to track their performance over time.
Lead Time for Changes is a measure of time taken by code changes to move from inception to deployment. It tracks the speed and efficiency of software delivery and provides valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies.
This metric measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process. It Helps assess how quickly the team can turn around tasks and features, identify trends and failures, and forecast how long future tasks will take.
Change Failure Rate measures the percentage of newly deployed changes that caused failure or glitches in production. It reflects reliability and efficiency and relates to team capacity, code complexity, and process efficiency, hence, impacting speed and quality.
The software performance Metrics quantitatively measure how well an individual, team, or organization performs in various aspects of their operations. They offer insights into how well goals and objectives are being met and highlight potential bottlenecks.
Deployment Frequency tracks how often the code is deployed to production. It measures the rate of change in software development and highlights potential issues. A key indicator of agility and efficiency, regular deployments indicate a streamlined pipeline, which further allows teams to deliver features and updates faster.
Mean Time to Restore measures the average time taken by a system or application to recover from any failure or incident. It highlights the efficiency and effectiveness of an organization’s incident response and resolution procedures.
Code Quality Metrics measure various aspects of the code quality within a software development project such as readability, maintainability, performance, and adherence to best practices. Some of the common metrics are:
Code coverage measures the percentage of a codebase that is tested by automated tests. It helps ensure that the tests cover a significant portion of the code, and identifies untested parts and potential bugs.
Code churn measures the frequency of changes made to a specific piece of code, such as a file, class, or function during development. High code churn suggests frequent modifications and potential instability, while low code churn usually reflects a more stable codebase but could also signal slower development progress.
Focus Metrics are KPIs that organizations prioritize to target specific areas of their operations or processes for improvement. They address particular challenges or goals within the software development projects or organization and offer detailed insights into targeted areas. Few metrics include:
Developer Workload represents the count of Issue tickets or Story points completed by each developer against the total Issue tickets/Story points assigned to them in the current sprint. It helps to understand how much work developers are handling, and crucial for balancing workloads, improving productivity, and preventing burnout.
Work progress represents the percentage breakdown of Issue tickets or Story points in the selected sprint according to their current workflow status. It highlights how much work the team handles at a given time, which further helps to maintain a smooth and productive workflow.
Customer Satisfaction tracks how happy or content customers are with a product, service, or experience. It usually involves users' feedback through various methods and analyzing that data to understand their satisfaction level.
Technical Debt metrics measure and manage the cost and impact of technical debt in the software development lifecycle. It helps to ensure that most critical issues are addressed first, provides insights into the cost associated with maintaining and fixing technical debt, and identifies areas of the codebase that require improvement.
Test coverage measures percentage of the codebase or features covered by tests. It ensure that tests are comprehensive and can identify potential issues within the codebase which further improves quality and fewer bugs.
This metric measures the number of defects found per unit of code or functionality (e.g., defects per thousand lines of code). It helps to assess the code quality and the effectiveness of the testing process.
This metric tracks the proportion of test cases that are automated compared to those that are manual. It offers insight into the extent to which automation is integrated into the testing process and assess the efficiency and effectiveness of testing practices.
This software metric helps to measure how efficiently dev teams or individuals are working. Productivity metrics provide insights into various aspects of productivity. Some of the metrics are:
This metric measures how long it takes for code reviews to be completed from the moment a PR or code change is submitted until it is approved and merged. Regular and timely reviews foster better collaboration between team members, contribute to higher code quality by catching issues early, and ensure adherence to coding standards.
Sprint Burndown tracks the amount of work remaining in a sprint versus time for scrum teams. It helps development teams visualize progress and productivity throughout a sprint, helps identify potential issues early, and stay focused.
Operational Metrics are key performance indicators that provide insights into operational performance aspects, such as productivity, efficiency, and quality. They focus on the routine activities and processes that drive business operations and help to monitor, manage, and optimize operational performance. These metrics are:
Incident Frequency tracks how often incidents or outages occur in a system or service. It helps to understand and mitigate disruptions in system operations. High Incident Frequency indicates frequent disruptions, while low incident frequency suggests a stable system but requires verification to ensure incidents aren’t underreported.
Error Rate measures the frequency of errors occurring in the system, typically expressed as errors per transaction, request, or unit of time. It helps gauge system reliability and quality and highlights issues in performance or code that need addressing to improve overall stability.
The mean Time between Failures tracks the average time between system failures. It signifies how often the failures are expected to occur in a given period. High MTBF indicates that the software is less reliable and needs less frequent maintenance.
Security Metrics evaluate the effectiveness of an organization's security posture and its ability to protect information and systems from threats. They provide insights into understanding how well security measures function, identify vulnerabilities, and security control effectiveness. Key metrics are:
Mean Time to Detect tracks how long a team takes to detect threats. The longer the threat is unidentified, there is a high chance of an escalated problem. MTTD helps minimize the issues' impact in the early stages and refine monitoring and alert processes.
The number of Vulnerabilities measures the total vulnerabilities identified in the codebase. It assesses the system’s security posture, and remediation efforts and provides insights into the impact of security practices and tools.
Mean Time to Patch reflects the time taken to fix security vulnerabilities, soft bugs, or other security issues. It assesses how quickly an organization can respond and manage vulnerabilities in the software delivery processes.
Software development metrics play a vital role in aligning software development projects with business goals. These metrics help guide software engineers in making data-driven decisions and process improvements and ensure that projects progress smoothly, boost team performance, meet user needs, and drive overall success. Regularly analyzing these metrics optimizes development processes, manages technical debt, and ultimately delivers high-quality software to the end-users.
Software development is an ever-evolving field that thrives on teamwork, collaboration, and productivity. Many organizations started shifting towards DORA metrics to measure their development processes as these metrics are like the golden standards of software delivery performance.
But here’s the thing: Focusing solely on DORA Metrics isn’t just enough! Teams need to dig deep and uncover the root causes of any pesky issues affecting their metrics.
Enter the notorious world of underlying indicators! These troublesome signs point to deeper problems lurking in the development process that can drag down DORA metrics. Identifying and tackling these underlying issues helps to improve their development processes and, in turn, boost their DORA metrics.
In this blog post, we’ll dive into the uneasy relationship between these indicators and DORA Metrics, and how addressing them can help teams elevate their software delivery performance.
Developed by the DevOps Research and Assessment team, DORA Metrics are key performance indicators that measure the effectiveness and efficiency of software development and delivery processes. With its data-driven approach, software teams can evaluate of the impact of operational practices on software delivery performance.
In 2021, the DORA Team added Reliability as a fifth metric. It is based upon how well the user’s expectations are met, such as availability and performance, and measures modern operational practices.
Deployment Frequency measures how often a team deploys code to production. Symptoms affecting this metric include:
Lead Time for Changes measures the time taken from code commit to deployment. Symptoms impacting this metric include:
Change Failure Rate indicates the percentage of changes that result in failures. Symptoms affecting this metric include:
Mean Time to Restore Service measures how long it takes to recover from a failure. Symptoms impacting this metric include:
Software analytics tools are an effective way to measure DORA DevOps metrics. These tools can automate data collection from various sources and provide valuable insights. They also offer centralized dashboards for easy visualization and analysis to identify bottlenecks and inefficiencies in the software delivery process. They also facilitate benchmarking against industry standards and previous performance to set realistic improvement goals. These software analytics tools promote collaboration between development and operations by providing a common framework for discussing performance. Hence, enhancing the ability to make data-driven decisions, drive continuous improvement, and improve customer satisfaction.
Typo is a powerful software engineering platform that enhances SDLC visibility, provides developer insights, and automates workflows to help you build better software faster. It integrates seamlessly with tools like GIT, issue trackers, and CI/CD systems. It offers a single dashboard with key DORA and other engineering metrics — providing comprehensive insights into your deployment process. Additionally, Typo includes engineering benchmarks for comparing your team's performance across industries.
DORA metrics are essential for evaluating software delivery performance, but they reveal only part of the picture. Addressing underlying issues affecting these metrics such as high deployment frequency or lengthy change lead time, can lead to significant improvements in software quality and team efficiency.
Use tools like Typo to gain deeper insights and benchmarks, enabling more effective performance enhancements.
The SPACE framework is a multidimensional approach to understanding and measuring developer productivity. Since the teams are increasingly distributed and users demand efficient and high-quality software, the SPACE framework provides a structured way to assess productivity beyond traditional metrics.
In this blog post, we highlight the importance of the SPACE framework dimensions for software teams and explore its components, benefits, and practical applications.
The SPACE framework is a multidimensional approach to measuring developer productivity. Below are five SPACE framework dimensions:
By examining these dimensions, the SPACE framework provides a comprehensive view of developer productivity that goes beyond traditional metrics.
The SPACE productivity framework is important for software development teams because it provides an in-depth understanding of productivity, significantly improving both team dynamics and software quality. Here are specific insights into how the SPACE framework benefits software teams:
Focusing on satisfaction and well-being allows software engineering leaders to create a positive work environment. It is essential to retain top talent as developers who feel valued and supported are more likely to stay with the organization.
Metrics such as employee satisfaction surveys and burnout assessments can highlight potential bottlenecks. For instance, if a team identifies low satisfaction scores, they can implement initiatives like team-building activities, flexible work hours, or mental health resources to increase morale.
Emphasizing performance as an outcome rather than just output helps teams better align their work with business goals. This shift encourages developers to focus on delivering high-quality code that meets customer needs.
Performance metrics might include customer satisfaction ratings, bug counts, and the impact of features on user engagement. For example, a team that measures the effectiveness of a new feature through user feedback can make informed decisions about future development efforts.
The activity dimension provides valuable insights into how developers spend their time. Tracking various activities such as coding, code reviews, and collaboration helps in identifying bottlenecks and inefficiencies in their processes.
For example, if a team notices that code reviews are taking too long, they can investigate the reasons behind the delays and implement strategies to streamline the review process, such as establishing clearer guidelines or increasing the number of reviewers.
Effective communication and collaboration are crucial for successful software development. The SPACE framework fosters teams to assess their communication practices and identify potential bottlenecks.
Metrics such as the speed of integrating work, the quality of peer reviews, and the discoverability of documentation reveal whether team members are able to collaborate well. Suppose, the team finds that onboarding new members takes too long. To improvise, they can enhance their documentation and mentorship programs to facilitate smoother transitions.
The efficiency and flow dimension focuses on minimizing interruptions and maximizing productive time. By identifying and addressing factors that disrupt workflow, teams can create an environment conducive to deep work.
Metrics such as the number of interruptions, the time spent in value-adding activities, and the lead time for changes can help teams pinpoint inefficiencies. For example, a team may discover that frequent context switching between tasks is hindering productivity and can implement strategies like time blocking to improve focus.
The SPACE framework promotes alignment between team efforts and organizational objectives. Measuring productivity in terms of business outcomes can ensure that their work contributes to overall success.
For instance, if a team is tasked with improving user retention, they can focus their efforts on developing features that enhance the user experience. They can further measure their impact through relevant metrics.
The rise of remote and hybrid models results in evolvement in the software development landscape. The SPACE framework offers the flexibility to adapt to new challenges.
Teams can tailor their metrics to the unique dynamics of their work environment. So, they remain relevant and effective. For example, in a remote setting, teams might prioritize communication metrics so that collaboration remains strong despite physical distance.
Implementing the SPACE framework encourages a culture of continuous improvement within software development teams. Regularly reviewing productivity metrics and discussing them openly help to identify areas for growth and innovation.
It fosters an environment where feedback is valued, team members feel heard and empowered to contribute to increasing productivity.
The SPACE framework helps bust common myths about productivity, such as more activity equates to higher productivity. Providing a comprehensive view of productivity that includes satisfaction, performance, and collaboration can avoid the pitfalls of relying on simplistic metrics. Hence, fostering a more informed approach to productivity measurement and management.
Ultimately, the SPACE framework recognizes that developer well-being is integral to productivity. By measuring satisfaction and well-being alongside performance and activity, teams can create a holistic view of productivity that prioritizes the health and happiness of developers.
This focus on well-being not only enhances individual performance but also contributes to a positive team culture and overall organizational success.
Implementing the SPACE framework effectively requires a structured approach. It blends the identification of relevant metrics, the establishment of baselines, and the continuous improvement culture. Here’s a detailed guide on how software teams can adopt the SPACE framework to enhance their productivity:
To begin, teams must establish specific, actionable metrics for each of the five dimensions of the SPACE framework. This involves not only selecting metrics but also ensuring they are tailored to the team’s unique context and goals. Here are some examples for each dimension:
Once metrics are defined, teams should establish baselines for each metric. This involves collecting initial data to understand current performance levels. For example, a team measures the time taken for code reviews. They should gather data over several sprints to determine the average time before setting improvement goals.
Setting SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals based on these baselines enables teams to track progress effectively. For instance, if the average code review time is currently two days, a goal might be to reduce this to one day within the next quarter.
Foster a culture of open communication for the SPACE framework to be effective. Team members should feel comfortable discussing productivity metrics and sharing feedback. A few of the ways to do so include conducting regular team meetings where metrics are reviewed, challenges are addressed and successes are celebrated.
Encouraging transparency around metrics helps illustrate productivity measurements and ensures that all team members understand the rationale behind them. For instance, developers are aware that a high number of pull requests is not the sole indicator of productivity. This allows them to feel less pressure to increase activity without considering quality.
The SPACE framework's effectiveness relies on two factors: continuous evaluation and adaptation of the chosen metrics. Scheduling regular reviews (e.g., quarterly) allows to assess whether the metrics are providing meaningful insights and they need to be adjusted.
For example, a metric for measuring developer satisfaction reveals consistently low scores. Hence, the team should investigate the underlying causes and consider implementing changes, such as additional training or resources.
To ensure that the SPACE framework is not just a theoretical exercise, teams should integrate the metrics into their daily workflows. This can be achieved through:
Implementing the SPACE framework should be viewed as an ongoing journey rather than a one-time initiative. Encourage a culture of continuous learning where team members are motivated to seek out knowledge and improve their practices.
This can be facilitated through:
Utilizing technology tools can streamline the implementation of the SPACE framework. Tools that facilitate project management, code reviews, and communication can provide valuable data for the defined metrics. For example:
While the SPACE framework focuses on the importance of satisfaction and well-being, software teams should actively measure the impact of their initiatives on these dimensions. A few of the ways include follow-up surveys and feedback sessions after implementing changes.
Suppose, a team introduces mental health days. They should assess whether this leads to increased satisfaction scores or reduced burnout levels in subsequent surveys.
Recognizing and appreciating software developers helps to maintain morale and motivation within the team. The achievements should be acknowledged when teams achieve their goals related to the SPACE framework, including improved performance metrics or higher satisfaction scores.
On the other hand, when challenges arise, teams should adopt a growth mindset and view failures as opportunities for learning and improvement. Conducting post-mortems on projects that did not meet expectations helps teams identify what went wrong and how to fix it in the future.
Finally, the implementation of the SPACE productivity framework should be iterative. Teams gaining experience with the framework should continuously refine their approach based on feedback and results. It ensures that the framework remains relevant and effective in addressing the evolving needs of the development team and the organization.
Typo is a popular software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation for building high-performing tech teams.
Here’s how Typo metrics fit into the SPACE framework's different dimensions:
Satisfaction and Well-Being: With the Developer Experience feature, which includes focus and sub-focus areas, engineering leaders can monitor how developers feel about working at the organization, assess burnout risk, and identify necessary improvements.
The automated code review tool auto-analyzes the codebase and pull requests to identify issues and auto-generate fixes before merging to master. This enhances satisfaction by ensuring quality and fostering collaboration.
Performance: The sprint analysis feature provides in-depth insights into the number of story points completed within a given time frame. It tracks and analyzes the team's progress throughout a sprint, showing the amount of work completed, work still in progress, and the remaining time. Typo’s code review tool understands the context of the code and quickly finds and fixes issues accurately. It also standardizes code, reducing the risk of security breaches and improving maintainability.
Activity: Typo measures developer activity through various metrics:
Communication & Collaboration: Code coverage measures the percentage of the codebase tested by automated tests, while code reviews provide feedback on their effectiveness. PR Merge Time represents the average time taken from the approval of a Pull Request to its integration into the main codebase.
Efficiency and Flow: Typo assesses this dimension through two major metrics:
By following the above-mentioned steps, dev teams can effectively implement the SPACE metrics framework to enhance productivity, improve developer satisfaction, and align their efforts with organizational goals. This structured approach not only encourages a healthier work culture but also drives better outcomes in software development.
Software development teams are key indicators of an organization’s success. With the ever-evolving tech landscape, it is crucial to understand how well they are performing and what needs to be fixed.
This is how software development analytics tools came to the rescue. These tools provide insights into various metrics related to the development workflow, measure progress, and make informed decisions.
One such tool is Pluralsight Flow which is popular in development teams nowadays. But, will it work for you? Maybe. Maybe not!
So, worry not! We have curated the top 5 Pluralsight Flow alternatives that you can take note of when considering software development analytics tools for your organization.
Pluralsight Flow, a leading engineering analytics platform, aggregates GIT data into comprehensive insights. It gathers important engineering metrics such as DORA metrics code commits and pull requests, all displayed in a centralized dashboard. It can be integrated with manual and automated testing such as Azure DevOps and GitLab.
However, there is a lack of support for certain programming languages and the cost is considered high, with no free trial available.
Given the downsides of Pluralsight Flow, there are other top alternatives available in the market. Take a look below:
Typo is an AI-driven engineering analytics platform that offers SDLC visibility, data-driven insights, and workflow automation for software teams. It provides comprehensive insights through DORA and other engineering metrics in a centralized dashboard. Typo’s pre-built integration in the dev tool stack helps to highlight developer blockers, predict sprint delays, and measure business impact. Its automated code tool allows engineering teams to identify issues and auto-fix them before merging to master. Typo’s holistic framework captures developer experience to understand what causes friction and ways to improve.
LinearB is a real-time performance analysis tool that measures Git data, tracks DORA metrics, and collects data from other tools. It highlights automatable tasks to software teams, helping them save time and resources. Its project delivery forecast allows the team to stay on schedule and communicate project delivery status updates. It can also be integrated with third-party applications such as Jira, Slack, Shortcut, and other popular tools.
Jellyfish is an engineering management platform that aligns software engineering insights with company goals. It translates tech data into reports and insights for management and leadership to understand easily. It provides real-time visibility into engineering work quickly and allows the team members to track key metrics such as PR statuses, code commits, and overall project progress. Jellyfish offers multiple perspectives on resource allocation and helps to track investments made during product development.
Waydev is an analytics intelligence platform that uses the agile method to track the software development team output. It primarily focuses on market-based metrics and allows engineering leaders to see data from different perspectives. It offers tailored reports that help in aligning engineering output with business goals. Waydev also gives the cost and progress of delivery and key initiatives. It can be seamlessly integrated with third-party tools including Gitlab, Github, and CircleCI.
Swarmia is an engineering effectiveness platform that offers visibility into three key areas: business outcome, developer productivity, and developer experience. Its automation feature allows all tasks to be assigned to the appropriate issues and concerned person. It also has a working agreement feature that helps to set numerical targets for activities like managing open pull requests and code reviews.
While we have shared top software development analytics tools, don’t forget to conduct thorough research before selecting for your engineering team. Check whether it aligns well with your requirements, facilitates team collaboration and continuous improvement, integrates seamlessly with your existing and upcoming tools, and so on.
All the best!
Lots of organizations are prioritizing the adoption and enhancement of their DevOps practices. The aim is to optimize the software development life cycle and increase delivery speed which enables faster market reach and improved customer service.
In this article, we’ve shared four key DevOps metrics, their importance and other metrics to consider.
DevOps metrics are the key indicators that showcase the performance of the DevOps software development pipeline. By bridging the gap between development and operations, these metrics are essential for measuring and optimizing the efficiency of both processes and people involved.
Tracking DevOps metrics allows teams to quickly identify and eliminate bottlenecks, streamline workflows, and ensure alignment with business objectives.
Here are four important DevOps metrics to consider:
Deployment Frequency measures how often code is deployed into production per week, taking into account everything from bug fixes and capability improvements to new features. It is a key indicator of agility, and efficiency and a catalyst for continuous delivery and iterative development practices that align seamlessly with the principles of DevOps. A wrong approach in the first key metric can degrade the other DORA metrics.
Deployment Frequency is measured by dividing the number of deployments made during a given period by the total number of weeks/days. One deployment per week is standard. However, it also depends on the type of product.
Lead Time for Changes measures the time it takes for a code change to go through the entire development pipeline and become part of the final product. It is a critical metric for tracking the efficiency and speed of software delivery. The measurement of this metric offers valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies.
To measure this metric, DevOps should have:
Divide the total sum of time spent from commitment to deployment by the number of commitments made.
Change Failure Rate refers to the proportion or percentage of deployments that result in failure or errors, indicating the rate at which changes negatively impact the stability or functionality of the system. It reflects the stability and reliability of the entire software development and deployment lifecycle. Tracking CFR helps identify bottlenecks, flaws, or vulnerabilities in processes, tools, or infrastructure that can negatively impact the quality, speed, and cost of software delivery.
To calculate CFR, follow these steps:
Apply the formula:
Use the formula CFR = (Number of Failed Changes / Total Number of Changes) * 100 to calculate the Change Failure Rate as a percentage.
Mean Time to Restore (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week. Measuring "Mean Time to Restore" (MTTR) provides crucial insights into an engineering team's incident response and resolution capabilities. It helps identify areas of improvement, optimize processes, and enhance overall team efficiency.
To calculate this, add the total downtime and divide it by the total number of incidents that occurred within a particular period.
Apart from the above-mentioned key metrics, there are other metrics to take into account. These are:
Cycle time measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process.
Mean Time to Failure (MTTF) is a reliability metric used to measure the average time a non-repairable system or component operates before it fails.
Error Rates measure the number of errors encountered in the platform. It identifies the stability, reliability, and user experience of the platform.
Response time is the total time from when a user makes a request to when the system completes the action and returns a result to the user.
Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics. It provides an efficient solution for development teams seeking precision in their DevOps performance measurement.
Adopting and enhancing DevOps practices is essential for organizations that aim to optimize their software development lifecycle. Tracking these DevOps metrics helps teams identify bottlenecks, improve efficiency, and deliver high-quality products faster.
In today's software development landscape, effective collaboration among teams and seamless service orchestration are essential. Achieving these goals requires adherence to organizational standards for quality, security, and compliance. Without diligent monitoring, organizations risk losing sight of their delivery workflows, complicating the assessment of impacts on release velocity, stability, developer experience, and overall application performance.
To address these challenges, many organizations have begun tracking DevOps Research and Assessment (DORA) metrics. These metrics provide crucial insights for any team involved in software development, offering a comprehensive view of the Software Development Life Cycle (SDLC). DORA metrics are particularly useful for teams practising DevOps methodologies, including Continuous Integration/Continuous Deployment (CI/CD) and Site Reliability Engineering (SRE), which focus on enhancing system reliability.
However, the collection and analysis of these metrics can be complex. Decisions about which data points to track and how to gather them often fall to individual team leaders. Additionally, turning this data into actionable insights for engineering teams and leadership can be challenging.
The DORA research team at Google conducts annual surveys of IT professionals to gather insights into industry-wide software delivery practices. From these surveys, four key metrics have emerged as indicators of software teams' performance, particularly regarding the speed and reliability of software deployment. These key DORA metrics include:
DORA metrics connect production-based metrics with development-based metrics, providing quantitative measures that complement qualitative insights into engineering performance. They focus on two primary aspects: speed and stability. Deployment frequency and lead time for changes relate to throughput, while time to restore services and change failure rate address stability.
Contrary to the historical view that speed and stability are opposing forces, research from DORA indicates a strong correlation between these metrics in terms of overall performance. Additionally, these metrics often correlate with key indicators of system success, such as availability, thus offering insights that benefit application performance, reliability, delivery workflows, and developer experience.
While DORA DevOps metrics may seem straightforward, measuring them can involve ambiguity, leading teams to make challenging decisions about which data points to use. Below are guidelines and best practices to ensure accurate and actionable DORA metrics.
Establishing a standardized process for monitoring DORA metrics can be complicated due to differing internal procedures and tools across teams. Clearly defining the scope of your analysis—whether for a specific department or a particular aspect of the delivery process—can simplify this effort. It’s essential to consider the type and amount of work involved in different analyses and standardize data points to align with team, departmental, or organizational goals.
For example, platform engineering teams focused on improving delivery workflows may prioritize metrics like deployment frequency and lead time for changes. In contrast, SRE teams focused on application stability might prioritize change failure rate and time to restore service. By scoping metrics to specific repositories, services, and teams, organizations can gain detailed insights that help prioritize impactful changes.
Best Practices for Defining Scope:
To maintain consistency in collecting DORA metrics, address the following questions:
1. What constitutes a successful deployment?
Establish clear criteria for what defines a successful deployment within your organization. Consider the different standards various teams might have regarding deployment stages. For instance, at what point do you consider a progressive release to be "executed"?
2. What defines a failure or response?
Clarify definitions for system failures and incidents to ensure consistency in measuring change failure rates. Differentiate between incidents and failures based on factors such as application performance and service level objectives (SLOs). For example, consider whether to exclude infrastructure-related issues from DORA metrics.
3. When does an incident begin and end?
Determine relevant data points for measuring the start and resolution of incidents, which are critical for calculating time to restore services. Decide whether to measure from when an issue is detected, when an incident is created, or when a fix is deployed.
4. What time spans should be used for analysis?
Select appropriate time frames for analyzing data, taking into account factors like organization size, the age of the technology stack, delivery methodology, and key performance indicators (KPIs). Adjust time spans to align with the frequency of deployments to ensure realistic and comprehensive metrics.
Best Practices for Standardizing Data Collection:
Before diving into improvements, it’s crucial to establish a baseline for your current continuous integration and continuous delivery performance using DORA metrics. This involves gathering historical data to understand where your organization stands in terms of deployment frequency, lead time, change failure rate, and MTTR. This baseline will serve as a reference point to measure the impact of any changes you implement.
Actionable Insights: If your deployment frequency is low, it may indicate issues with your CI/CD pipeline or development process. Investigate potential causes, such as manual steps in deployment, inefficient testing procedures, or coordination issues among team members.
Strategies for Improvement:
Actionable Insights: Long change lead time often points to inefficiencies in the development process. By analyzing your CI/CD pipeline, you can identify delays caused by manual approval processes, inadequate testing, or other obstacles.
Strategies for Improvement:
Actionable Insights: A high change failure rate is a clear sign that the quality of code changes needs improvement. This can be due to inadequate testing or rushed deployments.
Strategies for Improvement:
Actionable Insights: If your MTTR is high, it suggests challenges in incident management and response capabilities. This can lead to longer downtimes and reduced user trust.
Strategies for Improvement:
Utilizing DORA metrics is not a one-time activity but part of an ongoing process of continuous improvement. Establish a regular review cycle where teams assess their DORA metrics and adjust practices accordingly. This creates a culture of accountability and encourages teams to seek out ways to improve their CI/CD workflows continually.
Etsy, an online marketplace, adopted DORA metrics to assess and enhance its CI/CD workflows. By focusing on improving its deployment frequency and lead time for changes, Etsy was able to increase deployment frequency from once a week to multiple times a day, significantly improving responsiveness to customer needs.
Flickr used DORA metrics to track its change failure rate. By implementing rigorous automated testing and post-mortem analysis, Flickr reduced its change failure rate significantly, leading to a more stable production environment.
Google's Site Reliability Engineering (SRE) teams utilize DORA metrics to inform their practices. By focusing on MTTR, Google has established an industry-leading incident response culture, resulting in rapid recovery from outages and high service reliability.
Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics. It provides an efficient solution for development teams seeking precision in their DevOps performance measurement.
DORA metrics serve as a compass for engineering teams, optimizing development and operations processes to enhance efficiency, reliability, and continuous improvement in software delivery.
In this blog, we explore how DORA metrics boost tech team performance by providing critical insights into software development and delivery processes.
DORA metrics, developed by the DevOps Research and Assessment team, are a set of key performance indicators that measure the effectiveness and efficiency of software development and delivery processes. They provide a data-driven approach to evaluate the impact of operational practices on software delivery performance.
In 2021, the DORA Team added Reliability as a fifth metric. It is based upon how well the user’s expectations are met, such as availability and performance, and measures modern operational practices.
Here’s how key DORA metrics help in boosting performance for tech teams:
Deployment Frequency is used to track the rate of change in software development and to highlight potential areas for improvement. A wrong approach in the first key metric can degrade the other DORA metrics.
One deployment per week is standard. However, it also depends on the type of product.
Lead Time for Changes is a critical metric used to measure the efficiency and speed of software delivery. It is the duration between a code change being committed and its successful deployment to end-users.
The standard for Lead time for Change is less than one day for elite performers and between one day and one week for high performers.
CFR, or Change Failure Rate measures the frequency at which newly deployed changes lead to failures, glitches, or unexpected outcomes in the IT environment.
0% - 15% CFR is considered to be a good indicator of code quality.
MTTR, which stands for Mean Time to Recover, is a valuable metric that provides crucial insights into an engineering team's incident response and resolution capabilities.
Less than one hour is considered to be a standard for teams.
Firstly, you need to collect DORA Metrics effectively. This can be done by integrating tools and systems to gather data on key DORA metrics. There are various DORA metrics trackers in the market that make it easier for development teams to automatically get visual insights in a single dashboard. The aim is to collect the data consistently over time to establish trends and benchmarks.
The next step is to analyze them to understand your development team's performance. Start by comparing metrics to the DORA benchmarks to see if the team is an Elite, High, Medium, or Low performer. Ensure to look at the metrics holistically as improvements in one area may come at the expense of another. So, always strive for balanced improvements. Regularly review the collected metrics to identify areas that need the most improvement and prioritize them first. Don’t forget to track the metrics over time to see if the improvement efforts are working.
Leverage the DORA metrics to drive continuous improvement in engineering practices. Discuss what’s working and what’s not and set goals to improve metric scores over time. Don’t use DORA metrics on a sole basis. Tie it with other engineering metrics to measure it holistically and experiment with changes to tools, processes, and culture.
Encourage practices like:
Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics, providing an alternative and efficient solution for development teams seeking precision in their DevOps performance measurement.
DORA metrics are not just metrics; they are strategic navigators guiding tech teams toward optimized software delivery. By focusing on key DORA metrics, tech teams can pinpoint bottlenecks and drive sustainable performance enhancements.
The DORA (DevOps Research and Assessment) metrics have emerged as a north star for assessing software delivery performance. The fifth metric, Reliability is often overlooked as it was added after the original announcement of the DORA research team.
In this blog, let’s explore Reliability and its importance for software development teams.
DevOps Research and Assessment (DORA) metrics are a compass for engineering teams striving to optimize their development and operations processes.
In 2015, The DORA (DevOps Research and Assessment) team was founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren to evaluate and improve software development practices. The aim is to enhance the understanding of how development teams can deliver software faster, more reliably, and of higher quality.
Four key metrics are:
Reliability is a fifth metric that was added by the DORA team in 2021. It is based upon how well your user’s expectations are met, such as availability and performance, and measures modern operational practices. It doesn’t have standard quantifiable targets for performance levels rather it depends upon service level indicators or service level objectives.
While the first four DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recover) target speed and efficiency, reliability focuses on system health, production readiness, and stability for delivering software products.
Reliability comprises various metrics used to assess operational performance including availability, latency, performance, and scalability that measure user-facing behavior, software SLAs, performance targets, and error budgets. It has a substantial impact on customer retention and success.
A few indicators include:
These metrics provide a holistic view of software reliability by measuring different aspects such as failure frequency, downtime, and the ability to quickly restore service. Tracking these few indicators can help identify reliability issues, meet service level agreements, and enhance the software’s overall quality and stability.
The fifth DevOps metric, Reliability, significantly impacts overall performance. Here are a few ways:
Tracking reliability metrics like uptime, error rates, and mean time to recovery allows DevOps teams to proactively identify and address issues. Therefore, ensuring a positive customer experience and meeting their expectations.
Automating monitoring, incident response, and recovery processes helps DevOps teams to focus more on innovation and delivering new features rather than firefighting. This boosts overall operational efficiency.
Reliability metrics promote a culture of continuous learning and improvement. This breaks down silos between development and operations, fostering better collaboration across the entire DevOps organization.
Reliable systems experience fewer failures and less downtime, translating to lower costs for incident response, lost productivity, and customer churn. Investing in reliability metrics pays off through overall cost savings.
Reliability metrics offer valuable insights into system performance and bottlenecks. Continuously monitoring these metrics can help identify patterns and root causes of failures, leading to more informed decision-making and continuous improvement efforts.
The reliability metric with the other four DORA DevOps metrics offers a more comprehensive evaluation of software delivery performance. By focusing on system health, stability, and the ability to meet user expectations, this metric provides valuable insights into operational practices and their impact on customer satisfaction.
In software engineering, aligning your work with business goals is crucial. For startups, this is often straightforward. Small teams work closely together, and objectives are tightly aligned. However, in large enterprises where multiple teams are working on different products with varied timelines, this alignment becomes much more complex. In these scenarios, effective communication with leadership and establishing standard metrics to assess engineering performance is key. DORA Metrics is a set of key performance indicators that help organizations measure and improve their software delivery performance.
But first, let’s understand in brief how engineering works in startups vs. large enterprises -
In startups, small, cross-functional teams work towards a single goal: rapidly developing and delivering a product that meets market needs. The proximity to business objectives is close, and the feedback loop is short. Decision-making is quick, and pivoting based on customer feedback is common. Here, the primary focus is on speed and innovation, with less emphasis on process and documentation.
Success in a startup's engineering efforts can often be measured by a few key metrics: time-to-market, user acquisition rates, and customer satisfaction. These metrics directly reflect the company's ability to achieve its business goals. This simple approach allows for quick adjustments and real-time alignment of engineering efforts with business objectives.
Large enterprises operate in a vastly different environment. Multiple teams work on various products, each with its own roadmap, release schedules, and dependencies. The scale and complexity of operations require a structured approach to ensure that all teams align with broader organizational goals.
In such settings, communication between teams and leadership becomes more formalized, and standard metrics to assess performance and progress are critical. Unlike startups, where the impact of engineering efforts is immediately visible, large enterprises need a consolidated view of various performance indicators to understand how engineering work contributes to business objectives.
| Implementing DORA Metrics to Improve Dev Performance & Productivity?
Effective communication in large organizations involves not just sharing information but ensuring that it's understood and acted upon across all levels. Engineering teams must communicate their progress, challenges, and needs to leadership in a manner that is both comprehensive and actionable. This requires a common language of metrics that can accurately represent the state of development efforts.
Standard metrics are essential for providing this common language. They offer a way to objectively assess the performance of engineering teams, identify areas for improvement, and make informed decisions. However, the selection of these metrics is crucial. They must be relevant, actionable, and aligned with business goals.
DORA Metrics, developed by the DevOps Research and Assessment team, provide a robust framework for measuring the performance and efficiency of software delivery in DevOps and platform engineering. These metrics focus on key aspects of software development and delivery that directly impact business outcomes.
The four key DORA Metrics are:
These metrics provide a comprehensive view of the software delivery pipeline, from development to deployment and operational stability. By focusing on these key areas, organizations can drive improvements in their DevOps practices and enhance overall developer efficiency.
In large enterprises, the application of DORA DevOps Metrics can significantly improve developer efficiency and software delivery processes. Here’s how these key DORA metrics can be used effectively:
While DORA Metrics provide a solid foundation for measuring DevOps performance, they are not exhaustive. Integrating them with other software engineering metrics can provide a more holistic view of engineering performance. Below are use cases and some additional metrics to consider:
Software teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.
Low Deployment Frequency despite Swift Lead Time:
A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.
Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.
Abundant Comments per PR, Minimal Change Failure Rate:
Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.
Rapid post-review commits and a high deployment frequency reflect agile responsiveness to feedback. This iterative approach, driven by quick feedback incorporation, yields reliable releases, fostering customer trust and satisfaction.
Despite few post-review commits, high deployment frequency signals comprehensive pre-submission feedback integration. Emphasizing thorough code reviews assures stable deployments, showcasing the team’s commitment to quality.
Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.
A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.
The size of pull requests (PRs) profoundly influences deployment timelines. Correlating Large PR Size with Deployment Frequency enables teams to gauge the effect of extensive code changes on release cycles.
Maintaining a high deployment frequency with substantial PRs underscores effective testing and automation. Acknowledge this efficiency while monitoring potential code intricacies, ensuring stability amid complexity.
Infrequent deployments with large PRs might signal challenges in testing or review processes. Dividing large tasks into manageable portions accelerates deployments, addressing potential bottlenecks effectively.
PR size significantly influences code quality and stability. Analyzing Large PR Size alongside Change Failure Rate allows engineering leaders to assess the link between PR complexity and deployment stability.
Frequent deployment failures with extensive PRs indicate the need for rigorous testing and validation. Encourage breaking down large changes into testable units, bolstering stability and confidence in deployments.
A minimal failure rate with substantial PRs signifies robust testing practices. Focus on clear team communication to ensure everyone comprehends the implications of significant code changes, sustaining a stable development environment. Leveraging these correlations empowers engineering teams to make informed, data-driven decisions — a great way to drive business outcomes— optimizing workflows, and boosting overall efficiency. These insights chart a course for continuous improvement, nurturing a culture of collaboration, quality, and agility in software development endeavors.
By combining DORA Metrics with these additional metrics, organizations can gain a comprehensive understanding of their engineering performance and make more informed decisions to drive continuous improvement.
As organizations grow, the need for sophisticated tools to manage and analyze engineering metrics becomes apparent. This is where Software Engineering Intelligence (SEI) platforms come into play. SEI platforms like Typo aggregate data from various sources, including version control systems, CI/CD pipelines, project management tools, and incident management systems, to provide a unified view of engineering performance.
Benefits of SEI platforms include:
By leveraging SEI platforms, large organizations can harness the power of data to drive strategic decision-making and continuous improvement in their engineering practices.
| Implementing DORA Metrics to Improve Dev Performance & Productivity?
In large organizations, aligning engineering work with business goals requires effective communication and the use of standardized metrics. DORA Metrics provides a robust framework for measuring the performance of DevOps and platform engineering, enabling organizations to improve developer efficiency and software delivery processes. By integrating DORA Metrics with other software engineering metrics and leveraging Software Engineering Intelligence platforms, organizations can gain a comprehensive understanding of their engineering performance and drive continuous improvement.
Using DORA Metrics in large organizations not only helps in measuring and enhancing performance but also fosters a culture of data-driven decision-making, ultimately leading to better business outcomes. As the industry continues to evolve, staying abreast of best practices and leveraging advanced tools will be key to maintaining a competitive edge in the software development landscape.
The DevOps Research and Assessment (DORA) metrics have long served as a guiding light for organizations to evaluate and enhance their software development practices.
As we look to the future, what changes lie ahead for DORA metrics amidst evolving DevOps trends? In this blog, we will explore the future landscape and strategize how businesses can stay at the forefront of innovation.
The widely used reference book for engineering leaders called Accelerate introduced the DevOps Research and Assessment (DORA) group’s four metrics, known as the DORA 4 metrics.
These metrics were developed to assist engineering teams in determining two things:
Four key DevOps measurements:
Deployment Frequency measures the frequency of deployment of code to production or releases to end-users in a given time frame. Greater deployment frequency is an indication of increased agility and the ability to respond quickly to market demands.
Lead Time for Changes measures the time between a commit being made and that commit making it to production. Short lead times in software development are crucial for success in today’s business environment. When changes are delivered rapidly, organizations can seize new opportunities, stay ahead of competitors, and generate more revenue.
Change failure rate measures the proportion of deployment to production that results in degraded services. A lower change failure rate enhances user experience and builds trust by reducing failure and helping to allocate resources effectively.
Mean Time to Recover measures the time taken to recover from a failure, showing the team’s ability to respond to and fix issues. Optimizing MTTR aims to minimize downtime by resolving incidents through production changes and enhancing user satisfaction by reducing downtime and resolution times.
In 2021, DORA introduced Reliability as the fifth metric for assessing software delivery performance.
It measures modern operational practices and doesn’t have standard quantifiable targets for performance levels. Reliability comprises several metrics used to assess operational performance including availability, latency, performance, and scalability that measure user-facing behavior, software SLAs, performance targets, and error budgets.
DORA metrics play a vital role in measuring DevOps performance. It provides quantitative, actionable insights into the effectiveness of an organization’s software delivery and operational capabilities.
This further leads to:
One of the major predictions is that the use of DORA metrics in organizations will continue to rise. These metrics will broaden its horizons beyond five key metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, Mean Time to Restore, and Reliability) that focus on security, compliance, and more.
Organizations will start integrating these metrics with DevOps tools as well as tracking and reporting on these metrics to benchmark performance against industry leaders. This will allow software development teams to collect, analyze, and act on these data.
Observability and monitoring are now becoming two non-negotiable aspects of organizations. This is occurring as systems are getting more complex. This makes it challenging for them to understand the system’s state and diagnose issues without comprehensive observability.
Moreover, businesses have started relying on digital services which leads to an increase in the cost of downtime. Metrics like average detection and resolution times can pinpoint and rectify glitches in the early stages. Emphasizing these two aspects will further impact MTTR and CFR by enabling fast detection, and diagnosis of issues.
Nowadays, organizations are seeking more comprehensive and accurate metrics to measure software delivery performance. With the rise in adoption of DORA metrics, they are also said to be integrated well with the SPACE framework.
Since DORA and SPACE are both complemented in nature, integrating will provide a more holistic view. While DORA focuses on technical outcome and efficiency, the SPACE framework provides a broader perspective that incorporates aspects of developer satisfaction, collaboration, and efficiency (all about human factors). Together, they both emphasize the importance of continuous improvement and faster feedback loops.
AI and ML technologies are emerging. By integrating these tools with DORA metrics, development teams can leverage predictive analytics, proactively identify potential issues, and promote AI-driven decision-making.
DevOps gathers extensive data from diverse sources, which AI and ML tools can process and analyze more efficiently than manual methods. These tools enable software teams to automate decisions based on DORA metrics. For instance, if a deployment is forecasted to have a high failure rate, the tool can automatically initiate additional testing or notify the relevant team member.
Furthermore, continuous analysis of DORA metrics allows teams to pinpoint areas for improvement in the development and deployment processes. They can also create dashboards that highlight key metrics and trends.
DORA metrics alone are insufficient. Engineering teams need more than tools and processes. Soon, there will be a cultural transformation emphasizing teamwork, open communication, and collective accountability for results. Factors such as team morale, collaboration across departments, and psychological safety will be as crucial as operational metrics.
Collectively, these elements will facilitate data-driven decision-making, adaptability to change, experimentation with new concepts, and fostering continuous improvement.
As cyber-attacks continue to increase, security is becoming a critical concern for organizations. Hence, a significant upcoming trend is the integration of security with DORA metrics. This means not only implementing but also continually measuring and improving these security practices. Such integration aims to provide a comprehensive view of software development performance. This also allows striking a balance between speed and efficiency on one hand, and security and risk management on the other.
Ensure monitoring of industry trends, research, and case studies continuously related to DORA metrics and DevOps practices.
Don’t hesitate to pilot new DORA metrics and DevOps techniques within your organization to see what works best for your specific context.
Automate as much as possible in your software development and delivery pipeline to improve speed, reliability, and the ability to collect metrics effectively.
Foster collaboration between development, operations, and security teams to ensure alignment on DORA metrics goals and strategies.
Regularly review and optimize your DORA metrics implementation based on feedback and new insights gained from data analysis.
Promote a culture that values continuous improvement, learning, and transparency around DORA metrics to drive organizational alignment and success.
Typo is an effective software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation to build better programs faster. It offers comprehensive insights into the deployment process through key DORA metrics such as change failure rate, time to build, and deployment frequency.
Typo’s DORA metrics dashboard has a user-friendly interface and robust features tailored for DevOps excellence. The dashboard pulls in data from all the sources and presents it in a visualized and detailed way to engineering leaders and the development team.
Typo’s dashboard provides clear and intuitive visualizations of the four key DORA metrics: Deployment Frequency, Change Failure Rate, Lead Time for Changes, and Mean Time to Restore.
By providing benchmarks, Typo allows teams to compare their performance against industry standards, helping them understand where they stand. It also allows the team to compare their current performance with their historical data to track improvements or identify regressions.
Find out what it takes to build reliable high-velocity dev teams
The rising adoption of DORA metrics in DevOps marks a significant shift towards data-driven software delivery practices. Integrating these metrics with operations, tools, and cultural frameworks enhances agility and resilience. It is crucial to stay ahead of the curve by keeping an eye on trends, embracing automation, and promoting continuous improvement to effectively harness DORA metrics to drive innovation and achieve sustained success.
Cycle time is one of the important metrics in software development. It measures the time taken from the start to the completion of a process, providing insights into the efficiency and productivity of teams. Understanding and optimizing cycle time can significantly improve overall performance and customer satisfaction.
This blog will guide you through the precise cycle time calculation, highlighting its importance and providing practical steps to measure and optimize it effectively.
Cycle time measures the total elapsed time taken to complete a specific task or work item from the beginning to the end of the process.
It is important to differentiate cycle time from other related metrics such as lead time, which includes all delays and waiting periods, and takt time, which is the rate at which a product needs to be completed to meet customer demand. Understanding these differences is crucial for accurately measuring and optimizing cycle time.
To calculate total cycle time, you need to consider several components:
Step 1: Identify the start and end points of the process:
Clearly define the beginning and end of the process you are measuring. This could be initiating and completing a task in a project management tool.
Step 2: Gather the necessary data
Collect data on task durations and time tracking. Use tools like time-tracking software to ensure accurate data collection.
Step 3: Calculate net production time
Net production time is the total time available for production minus any non-productive time. For example, if a team works 8 hours daily but takes 1 hour for breaks and meetings, the net production time is 7 hours.
Step 4: Apply the cycle time formula
The formula for cycle time is:
Cycle Time = Net Production Time / Number of Work Items Completed
Cycle Time= Number of Work Items Completed / Net Production Time
Example calculation
If a team has a net production time of 35 hours in a week and completes 10 tasks, the cycle time is:
Cycle Time = 35 hours / 10 tasks = 3.5 hours per task
Cycle Time= 10 tasks / 35 hours =3.5 hours per task
An ideal cycle time should be less than 48 hours. Shorter cycle times in software development indicate that teams can quickly respond to requirements, deliver features faster, and adapt to changes efficiently, reflecting agile and responsive development practices.
Longer cycle times in software development typically indicates several potential issues or conditions within the development process. This can lead to increased costs and delayed delivery of features.
When calculating cycle time, it is crucial to account for variations in the complexity and size of different work items. Larger or more complex tasks can skew the average cycle time. To address this, categorize tasks by size or complexity and calculate cycle time for each category separately.
Control charts are a valuable tool for visualizing cycle time data and identifying trends or anomalies. You can quickly spot variations and investigate their causes by plotting cycle times on a control chart.
Performing statistical analysis on cycle time data can provide deeper insights into process performance. Metrics such as standard deviation and percentiles help understand the distribution and variability of cycle times, enabling more precise optimization efforts.
In order to effectively track task durations and completion times, it’s important to utilize time tracking tools and software such as Jira, Trello, or Asana. These tools can provide a systematic approach to managing tasks and projects by allowing team members to log their time and track task durations consistently.
Consistent data collection is essential for accurate time tracking. Encouraging all team members to consistently log their time and task durations ensures that the data collected is reliable and can be used for analysis and decision-making.
Visual management techniques, such as implementing Kanban boards or other visual tools, can be valuable for tracking progress and identifying bottlenecks in the workflow. These visual aids provide a clear and transparent view of task status and can help teams address any delays or issues promptly.
Optimizing cycle time involves analyzing cycle time data to identify bottlenecks in the workflow. By pinpointing areas where tasks are delayed, teams can take action to remove these bottlenecks and optimize their processes for improved efficiency.
Continuous improvement practices, such as implementing Agile and Lean methodologies, are effective for improving cycle times continuously. These practices emphasize a flexible and iterative approach to project management, allowing teams to adapt to changes and make continuous improvements to their processes.
Furthermore, studying case studies of successful cycle time reduction from industry leaders can provide valuable insights into efficient practices that have led to significant reductions in cycle times. Learning from these examples can inspire and guide teams in implementing effective strategies to reduce cycle times in their own projects and workflows.
Typo is an innovative tool designed to enhance the precision of cycle time calculations and overall productivity.
It seamlessly integrates Git data by analyzing timestamps from commits and merges. This integration ensures that cycle time calculations are based on actual development activities, providing a robust and accurate measurement compared to relying solely on task management tools. This empowers teams with actionable insights for optimizing their workflow and enhancing productivity in software development projects.
Here’s how Typo can help:
Automated time tracking: Typo provides automated time tracking for tasks, eliminating manual entry errors and ensuring accurate data collection.
Real-time analytics: With Typo, you can access real-time analytics to monitor cycle times, identify trends, and make data-driven decisions.
Customizable dashboards: Typo offers customizable dashboards that allow you to visualize cycle time data in a way that suits your needs, making it easier to spot inefficiencies and areas for improvement.
Seamless integration: Typo integrates seamlessly with popular project management tools, ensuring that all your data is synchronized and up-to-date.
Continuous improvement support: Typo supports continuous improvement by providing insights and recommendations based on your cycle time data, helping you implement best practices and optimize your workflows.
By leveraging Typo, you can achieve more precise cycle time calculations, improving efficiency and productivity.
In dealing with variability in task durations, it’s important to use averages as well as historical data to account for the range of possible durations. By doing this, you can better anticipate and plan for potential fluctuations in timing.
When it comes to ensuring data accuracy, it’s essential to implement a system for regularly reviewing and validating data. This can involve cross-referencing data from different sources and conducting periodic audits to verify its accuracy.
Additionally, when balancing speed and quality, the focus should be on maintaining high-quality standards while optimizing cycle time to ensure customer satisfaction. This can involve continuous improvement efforts aimed at increasing efficiency without compromising the quality of the final output.
Accurately calculating and optimizing cycle time is essential for improving efficiency and productivity. By following the steps outlined in this blog and utilizing tools like Typo, you can gain valuable insights into your processes and make informed decisions to enhance performance. Start measuring your cycle time today and reap the benefits of precise and optimized workflows.
As DevOps practices continue to evolve, it’s crucial for organizations to effectively measure DevOps metrics to optimize performance.
Here are a few common mistakes to avoid when measuring these metrics to ensure continuous improvement and successful outcomes:
In 2024, the landscape of DevOps metrics continues to evolve, reflecting the growing maturity and sophistication of DevOps practices. The emphasis is to provide actionable insights into the development and operational aspects of software delivery.
The integration of AI and machine learning (ML) in DevOps has become increasingly significant in transforming how teams monitor, manage, and improve their software development and operations processes. Apart from this, observability and real-time monitoring have become critical components of modern DevOps practices in 2024. They provide deep insights into system behavior and performance and are enhanced significantly by AI and ML technologies.
Lastly, Organizations are prioritizing comprehensive, real-time, and predictive security metrics to enhance their security posture and ensure robust incident response mechanisms.
DevOps metrics track both technical capabilities and team processes. They reveal the performance of a DevOps software development pipeline and help to identify and remove any bottlenecks in the process in the early stages.
Below are a few benefits of measuring DevOps metrics:
When clear objectives are not defined for development teams, they may measure metrics that do not directly contribute to strategic goals. This leads to scattered efforts and teams may achieve high numbers in certain metrics without realizing they are not contributing meaningfully to overall business objectives. This may also not provide actionable insights and decisions might be based on incomplete or misleading data. Lack of clear objectives makes it challenging to evaluate performance accurately and makes it unclear whether performance is meeting expectations or falling short.
Below are a few ways to define clear objectives for DevOps metrics:
Organizations usually focus on delivering products quickly rather than quality. However, speed and quality must work hand in hand. DevOps tasks must be accomplished by maintaining high standards and must be delivered to the end users on time. Due to this, the development team often faces intense pressure to deliver products or updates rapidly to stay competitive in the market. This can lead them to focus excessively on speed metrics, such as deployment frequency or lead time for changes, at the expense of quality metrics.
It is usually believed that the more metrics you track, the better you’ll understand DevOps processes. This leads to an overwhelming number of metrics, where most of them are redundant or not directly actionable. It usually occurs when there is no clear strategy or prioritization framework, leading teams to attempt to measure everything that further becomes difficult to manage and interpret. Moreover, it also results in tracking numerous metrics to show detailed performance, even if those metrics are not particularly meaningful.
Engineering leaders often believe that rewarding performance will motivate developers to work harder and achieve better results. However, this is not true. Rewarding specific metrics can lead to an overemphasis on those metrics at the expense of other important aspects of work. For example, focusing solely on deployment frequency might lead to neglecting code quality or thorough testing. This can also result in short-term improvements but leads to long-term problems such as burnout, reduced intrinsic motivation, and a decline in overall quality. Due to this, developers may manipulate metrics or take shortcuts to achieve rewarded outcomes, compromising the integrity of the process and the quality of the product.
Without continuous integration and testing, bugs and defects are more likely to go undetected until later stages of development or production, leading to higher costs and more effort to fix issues. It compromises the quality of the software, resulting in unreliable and unstable products that can damage the organization’s reputation. Moreover, it can result in slower progress over time due to the increased effort required to address accumulated technical debt and defects.
Below are a few important DevOps metrics:
Deployment Frequency measures the frequency of code deployment to production and reflects an organization’s efficiency, reliability, and software delivery quality. It is often used to track the rate of change in software development and highlight potential areas for improvement.
Lead Time for Changes is a critical metric used to measure the efficiency and speed of software delivery. It is the duration between a code change being committed and its successful deployment to end-users. This metric is a good indicator of the team’s capacity, code complexity, and efficiency of the software development process.
Change Failure Rate measures the frequency at which newly deployed changes lead to failures, glitches, or unexpected outcomes in the IT environment. It reflects the stability and reliability of the entire software development and deployment lifecycle. It is related to team capacity, code complexity, and process efficiency, impacting speed and quality.
Mean Time to Recover is a valuable metric that calculates the average duration taken by a system or application to recover from a failure or incident. It is an essential component of the DORA metrics and concentrates on determining the efficiency and effectiveness of an organization’s incident response and resolution procedures.
Optimizing DevOps practices requires avoiding common mistakes in measuring metrics. To optimize DevOps practices and enhance organizational performance, specialized tools like Typo can help simplify the measurement process. It offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.
Platform engineering tools empower developers by enhancing their overall experience. By eliminating bottlenecks and reducing daily friction, these tools enable developers to accomplish tasks more efficiently. This efficiency translates into improved cycle times and higher productivity.
In this blog, we explore top platform engineering tools, highlighting their strengths and demonstrating how they benefit engineering teams.
Platform Engineering, an emerging technology approach, enables the software engineering team with all the required resources. This is to help them perform end-to-end operations of software development lifecycle automation. The goal is to reduce overall cognitive load, enhance operational efficiency, and remove process bottlenecks by providing a reliable and scalable platform for building, deploying, and managing applications.
Typo is an effective software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation to build better programs faster. It can seamlessly integrate into tech tool stacks such as GIT versioning, issue tracker, and CI/CD tools.
It also offers comprehensive insights into the deployment process through key metrics such as change failure rate, time to build, and deployment frequency. Moreover, its automated code tool helps identify issues in the code and auto-fixes them before you merge to master.
Typo has an effective sprint analysis feature that tracks and analyzes the team’s progress throughout a sprint. Besides this, It also provides 360 views of the developer experience i.e. captures qualitative insights and provides an in-depth view of the real issues.
An open-source container orchestration platform. It is used to automate deployment, scale, and manage container applications.
Kubernetes is beneficial for application packages with many containers; developers can isolate and pack container clusters to be deployed on several machines simultaneously.
Through Kubernetes, engineering leaders can create Docker containers automatically and assign them based on demands and scaling needs.
Kubernetes can also handle tasks like load balancing, scaling, and service discovery for efficient resource utilization. It also simplifies infrastructure management and allows customized CI/CD pipelines to match developers’ needs.
An open-source automation server and CI/CD tool. Jenkins is a self-contained Java-based program that can run out of the box.
It offers extensive plug-in systems to support building and deploying projects. It supports distributing build jobs across multiple machines which helps in handling large-scale projects efficiently. Jenkins can be seamlessly integrated with various version control systems like Git, Mercurial, and CVS and communication tools such as Slack, and JIRA.
A powerful platform engineering tool that automates software development workflows directly from GitHub.GitHub Actions can handle routine development tasks such as code compilation, testing, and packaging for standardizedizing and efficient processes.
It creates custom workflows to automate various tasks and manage blue-green deployments for smooth and controlled application deployments.
GitHub Actions allows engineering teams to easily deploy to any cloud, create tickets in Jira, or publish packages.
GitLab CI automatically uses Auto DevOps to build, test, deploy, and monitor applications. It uses Docker images to define environments for running CI/CD jobs and build and publish them within pipelines. It supports parallel job execution that allows to running of multiple tasks concurrently to speed up build and test processes.
GitLab CI provides caching and artifact management capabilities to optimize build times and preserve build outputs for downstream processes. It can be integrated with various third-party applications including CircleCI, Codefresh, and YouTrack.
A Continuous Delivery platform provided by Amazon Web Services (AWS). AWS Codepipeline automates the release pipeline and accelerates the workflow with parallel execution.
It offers high-level visibility and control over the build, test, and deploy processes. It can be integrated with other AWS tools such as AWS Codebuild, AWS CodeDeploy, and AWS Lambda as well as third-party integrations like GitHub, Jenkins, and BitBucket.
AWS Codepipeline can also configure notifications for pipeline events to help stay informed about the deployment state.
A Github-based continuous deployment tool for Kubernetes application. Argo CD allows to deployment of code changes directly to Kubernetes resources.
It simplifies the management of complex application deployment and promotes a self-service approach for developers. Argo CD defines and automates the K8 cluster to suit team needs and includes multi-cluster setups for managing multiple environments.
It can seamlessly integrate with third-party tools such as Jenkins, GitHub, and Slack. Moreover, it supports multiple templates for creating Kubernetes manifests such as YAML files and Helm charts.
A CI/CD tool offered by Microsoft Azure. It supports building, testing, and deploying applications using CI/CD pipelines within the Azure DevOps ecosystem.
Azure DevOps Pipeline lets engineering teams define complex workflows that handle tasks like compiling code, running tests, building Docker images, and deploying to various environments. It can automate the software delivery process, reducing manual intervention, and seamlessly integrates with other Azure services, such as Azure Repos, Azure Artifacts, and Azure Kubernetes Service (AKS).
Moreover, it empowers DevSecOps teams with a self-service portal for accessing tools and workflows.
An Infrastructure as Code (IoC) tool. It is a well-known cloud-native platform in the software industry that supports multiple cloud provider and infrastructure technologies.
Terraform can quickly and efficiently manage complex infrastructure and can centralize all the infrastructures. It can seamlessly integrate with tools like Oracle Cloud, AWS, OpenStack, Google Cloud, and many more.
It can speed up the core processes the developers’ team needs to follow. Moreover, Terraform automates security based on the enforced policy as the code.
A platform-as-a-service (PaaS) based on a managed container system. Heroku enables developers to build, run, and operate applications entirely in the cloud and automates the setup of development, staging, and production environments by configuring infrastructure, databases, and applications consistently.
It supports multiple deployment methods, including Git, GitHub integration, Docker, and Heroku CLI, and includes built-in monitoring and logging features to track application performance and diagnose issues.
A popular Continuous Integration/Continuous Delivery (CI/CD) tool that allows software engineering teams to build, test, and deploy software using intelligent automation. It hosts CI under the cloud-managed option.
Circle CI is GitHub-friendly and includes extensive API for customized integrations. It supports parallelism i.e. splitting tests across different containers to run as clean and separate builds. It can also be configured to run complex pipelines.
Circle CI has an in-built feature ‘Caching’. It speeds up builds by storing dependencies and other frequently-used files, reducing the need to re-download or recompile them for subsequent builds.
Understand what specific problems or challenges the tools need to solve. This could include scalability, automation, security, compliance, etc. Consider inputs from stakeholders and other relevant teams to understand their requirements and pain points.
List out the essential features and capabilities needed in platform engineering tools. Also, the tools must integrate well with existing infrastructure, development methodologies (like Agile or DevOps), and technology stack.
Check if the tools have built-in security features or support integration with security tools for vulnerability scanning, access control, encryption, etc. The tools must comply with relevant industry regulations and standards applicable to your organization.
Check the availability and quality of documentation, tutorials, and support resources. Good support can significantly reduce downtime and troubleshooting efforts.
Choose tools that are flexible and adaptable to future technology trends and changes in the organization’s needs. The tools must integrate smoothly with the existing toolchain, including development frameworks, version control systems, databases, and cloud services.
Conduct a pilot or proof of concept to test how well the tools perform in the environment. This allows them to validate their suitability before committing to full deployment.
Platform engineering tools play a crucial role in the IT industry by enhancing the experience of software developers. They streamline workflows, remove bottlenecks, and reduce friction within developer teams, thereby enabling more efficient task completion and fostering innovation across the software development lifecycle.
In today's competitive tech landscape, engineering teams need robust and actionable metrics to measure and improve their performance. The DORA (DevOps Research and Assessment) metrics have emerged as a standard for assessing software delivery performance. In this blog, we'll explore what DORA metrics are, why they're important, and how to master their implementation to drive business success.
DORA metrics, developed by the DORA team, are key performance indicators that measure the performance of DevOps and engineering teams. They are the standard framework to track the effectiveness and efficiency of software development and delivery processes. Optimizing DORA Metrics helps achieve optimal speed, quality, and stability and provides a data-driven approach to evaluating the operational practices' impact on software delivery performance.
The four key DORA metrics are:
In 2021, the DORA Team added Reliability as a fifth metric. It is based upon how well the user’s expectations are met, such as availability and performance, and measures modern operational practices.
These metrics offer a comprehensive view of the software delivery process, highlighting areas for improvement and enabling software teams to enhance their delivery speed, reliability, and overall quality, leading to better business outcomes.
DORA metrics provide an objective way to measure the performance of software delivery processes. By focusing on these key indicators, dev teams gain a clear and quantifiable understanding of their tech practices.
DORA metrics enable organizations to benchmark their performance against industry standards. The DORA State of DevOps reports provide insights into what high-performing teams look like, offering a target for other organizations to aim for. By comparing your metrics against these benchmarks, you can set realistic goals and understand where your team stands to others in the industry.
DORA metrics promote better collaboration and communication within and across teams. By providing a common language and set of goals, these metrics align development, operations, and business teams around shared objectives. This alignment helps in breaking down silos and fostering a culture of collaboration and transparency.
The ultimate goal of tracking DORA metrics is to improve business outcomes. High-performing teams, as measured by DORA metrics, are correlated with faster delivery times, higher quality software, and improved stability. These improvements lead to greater customer satisfaction, increased market competitiveness, and higher revenue growth.
Analyzing DORA metrics helps DevOps teams identify performance trends and pinpoint bottlenecks in their software delivery lifecycle (SDLC). This allows them to address issues proactively, and improve developer experiences and overall workflow efficiency.
Integrating DORA metrics into value stream management practices enables organizations to optimize their software delivery processes. Analyzing DORA metrics allows teams to identify inefficiencies and bottlenecks in their value streams and inform teams where to focus their improvement efforts in the context of VSM.
Firstly, engineering leaders must identify what they want to achieve by tracking DORA metrics. Objectives might include increasing deployment frequency, reducing lead time, decreasing change failure rates, or minimizing MTTR.
Ensure your tools are properly configured to collect the necessary data for each metric:
Use dashboards and reports to visualize the metrics. There are many DORA metrics trackers available in the market. Do research and select a tool that can help you create clear and actionable visualizations.
Establish benchmarks based on industry standards or your historical data. Set realistic targets for improvement and use these as a guide for your DevOps practices.
Use the insights gained from your DORA metrics to identify bottlenecks and areas for improvement. Ensure to implement changes and continuously monitor their impact on your metrics. This iterative approach helps in gradually enhancing your DevOps performance.
Train software development teams on DORA metrics and promote a culture that values data-driven decision-making and learning from metrics. Also, encourage teams to discuss DORA metrics in retrospectives and planning meetings.
Regularly review metrics and adjust your practices as needed. The objectives and targets must evolve with the organization’s growth and changes in the industry. Typo is an intelligent engineering management platform for gaining visibility, removing blockers, and maximizing developer effectiveness. Its user-friendly interface and cutting-edge capabilities set it apart in the competitive landscape.
Understanding DORA metrics and effectively implementing and analyzing them can significantly enhance your software delivery performance and overall DevOps practices. These key metrics are vital for benchmarking against industry standards, enhancing collaboration and communication, and improving business outcomes.
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As a leading vendor in the software engineering intelligence (SEI) platform space, we at Typo, are pleased to present this summary report. This document synthesizes key findings from Gartner’s comprehensive analysis and incorporates our own insights to help you better understand the evolving landscape of SEI platforms. Our aim is to provide clarity on the benefits, challenges, and future directions of these platforms, highlighting their potential to revolutionize software engineering productivity and value delivery.
The Software Engineering Intelligence (SEI) platform market is rapidly growing, driven by the increasing need for software engineering leaders to use data to demonstrate their teams’ value. According to Gartner, this nascent market offers significant potential despite its current size. However, leaders face challenges such as fragmented data across multiple systems and concerns over adding new tools that may be perceived as micromanagement by their teams.
By 2027, the use of SEI platforms by software engineering organizations to increase developer productivity is expected to rise to 50%, up from 5% in 2024, driven by the necessity to deliver quantifiable value through data-driven insights.
Gartner defines SEI platforms as solutions that provide software engineering leaders with data-driven visibility into their teams’ use of time and resources, operational effectiveness, and progress on deliverables. These platforms must ingest and analyze signals from common engineering tools, offering tailored user experiences for easy data querying and trend identification.
There is growing interest in SEI platforms and engineering metrics. Gartner notes that client interactions on these topics doubled from 2022 to 2023, reflecting a surge in demand for data-driven insights in software engineering.
Existing DevOps and agile planning tools are evolving to include SEI-type features, creating competitive pressure and potential market consolidation. Vendors are integrating more sophisticated dashboards, reporting, and insights, impacting the survivability of standalone SEI platform vendors.
SEI platforms are increasingly incorporating AI to reduce cognitive load, automate tasks, and provide actionable insights. According to Forrester, AI-driven insights can significantly enhance software quality and team efficiency by enabling proactive management strategies.
Crucial for boosting developer productivity and achieving business outcomes. High-performing organizations leverage tools that track and report engineering metrics to enhance productivity.
SEI platforms can potentially replace multiple existing tools, serving as the main dashboard for engineering leadership. This consolidation simplifies the tooling landscape and enhances efficiency.
With increased operating budgets, there is a strong focus on tools that drive efficient and effective execution, helping engineering teams improve delivery and meet performance objectives.
Provide data-driven answers to questions about team activities and performance. Collecting and conditioning data from various engineering tools enables effective dashboards and reports, facilitating benchmarking against industry standards.
Generate insights through multivariate analysis of normalized data, such as correlations between quality and velocity. These insights help leaders make informed decisions to drive better outcomes.
Deliver actionable insights backed by recommendations. Tools may suggest policy changes or organizational structures to improve metrics like lead times. According to DORA, organizations leveraging key metrics like Deployment Frequency and Lead Time for Changes tend to have higher software delivery performance.
SEI platforms significantly enhance Developer Productivity by offering a unified view of engineering activities, enabling leaders to make informed decisions. Key benefits include:
SEI platforms provide a comprehensive view of engineering processes, helping leaders identify inefficiencies and areas for improvement.
By collecting and analyzing data from various tools, SEI platforms offer insights that drive smarter business decisions.
Organizations can use insights from SEI platforms to continually adjust and improve their processes, leading to higher quality software and more productive teams. This aligns with IEEE’s emphasis on benchmarking for achieving software engineering excellence.
SEI platforms enable benchmarking against industry standards, helping teams set realistic goals and measure their progress. This continuous improvement cycle drives sustained productivity gains.
Personalization and customization are critical for SEI platforms, ensuring they meet the specific needs of different user personas. Tailored user experiences lead to higher adoption rates and better user satisfaction, as highlighted by IDC.
The SEI platform market is poised for significant growth, driven by the need for data-driven insights into software engineering processes. These platforms offer substantial benefits, including enhanced visibility, data-driven decision-making, and continuous improvement. As the market matures, SEI platforms will become indispensable tools for software engineering leaders, helping them demonstrate their teams’ value and drive productivity gains.
SEI platforms represent a transformative opportunity for software engineering organizations. By leveraging these platforms, organizations can gain a competitive edge, delivering higher quality software and achieving better business outcomes. The integration of AI and machine learning further enhances these platforms’ capabilities, providing actionable insights that drive continuous improvement. As adoption increases, SEI platforms will play a crucial role in the future of software engineering, enabling leaders to make data-driven decisions and boost developer productivity.
In today’s software engineering, the pursuit of excellence hinges on efficiency, quality, and innovation. Engineering metrics, particularly the transformative DORA (DevOps Research and Assessment) metrics, are pivotal in gauging performance. According to the 2023 State of DevOps Report, high-performing teams deploy code 46 times more frequently and are 2,555 times faster from commit to deployment than their low-performing counterparts.
However, true excellence extends beyond DORA metrics. Embracing a variety of metrics—including code quality, test coverage, infrastructure performance, and system reliability—provides a holistic view of team performance. For instance, organizations with mature DevOps practices are 24 times more likely to achieve high code quality, and automated testing can reduce defects by up to 40%.
This benchmark report offers comprehensive insights into these critical metrics, enabling teams to assess performance, set meaningful targets, and drive continuous improvement. Whether you’re a seasoned engineering leader or a budding developer, this report is a valuable resource for achieving excellence in software engineering.
Velocity refers to the speed at which software development teams deliver value. The Velocity metrics gauge efficiency and effectiveness in delivering features and responding to user needs. This includes:
Quality represents the standard of excellence in development processes and code quality, focusing on reliability, security, and performance. It ensures that products meet user expectations, fostering trust and satisfaction. Quality metrics include:
Throughput measures the volume of features, tasks, or user stories delivered, reflecting the team’s productivity and efficiency in achieving objectives. Key throughput metrics are:
Collaboration signifies the cooperative effort among software development team members to achieve shared goals. It entails effective communication and collective problem-solving to deliver high-quality software products efficiently. Collaboration metrics include:
The benchmarks are organized into the following levels of performance for each metric:
These levels help teams understand where they stand in comparison to others and identify areas for improvement.
The data in the report is compiled from over 1,500 engineering teams and more than 2 million pull requests across the US, Europe, and Asia. This comprehensive data set ensures that the benchmarks are representative and relevant.
Engineering metrics serve as a cornerstone for performance measurement and improvement. By leveraging these metrics, teams can gain deeper insights into their processes and make data-driven decisions. This helps in:
Engineering metrics provide a valuable framework for benchmarking performance against industry standards. This helps teams:
Metrics also play a crucial role in enhancing team collaboration and communication. By tracking collaboration metrics, teams can:
Delivering quickly isn’t easy. It’s tough dealing with technical challenges and tight deadlines. But leaders in engineering guide their teams well. They encourage creativity and always look for ways to improve. Metrics are like helpful guides. They show us where we’re doing well and where we can do better. With metrics, teams set goals and see how they measure up to others. It’s like having a map to success.
With strong leaders, teamwork, and using metrics wisely, engineering teams can overcome challenges and achieve great things in software engineering. This Software Engineering Benchmarks Report provides valuable insights into their current performance, empowering them to strategize effectively for future success. Predictability is essential for driving significant improvements. A consistent workflow allows teams to make steady progress in the right direction.
By standardizing processes and practices, teams of all sizes can streamline operations and scale effectively. This fosters faster development cycles, streamlined processes, and high-quality code. Typo has saved significant hours and costs for development teams, leading to better quality code and faster deployments.
You can start building your metrics today with Typo for FREE. Our focus is to help teams ship reliable software faster.
Sprint Review Meetings are a cornerstone of Agile and Scrum methodologies, serving as a crucial touchpoint for teams to showcase their progress, gather feedback, and align on the next steps. However, many teams struggle to make the most of these meetings. This blog will explore how to enhance your Sprint Review Meetings to ensure they are effective, engaging, and productive.
The Sprint Review Meetings are meant to evaluate the progress made during a sprint, review the completed work, collect stakeholder feedback, and discuss the upcoming sprints. Key participants include the Scrum team, the Product Owner, key stakeholders, and occasionally the Scrum Master.
It’s important to differentiate Sprint Reviews from Sprint Retrospectives. While the former focuses on what was achieved and gathering feedback, the latter centers on process improvements and team dynamics.
Preparation can make or break a Sprint Review Meeting. Ensuring that the team is ready involves several steps.
Encouraging direct collaboration between stakeholders and teams is essential for the success of any project. It is important to create an environment where open communication is not only encouraged but also valued.
This means avoiding the use of excessive technical jargon, which can make non-technical stakeholders feel excluded. Instead, strive to facilitate clear and transparent communication that allows all voices to be heard and valued. Providing a platform for open and honest feedback will ensure that everyone’s perspectives are considered, leading to a more inclusive and effective collaborative process.
It is crucial to have a clearly defined agenda for a productive Sprint Review. This includes sharing the agenda well in advance of the meeting, and clearly outlining the main topics of discussion. It’s also important to allocate specific time slots for each segment of the meeting to ensure that the review remains efficient.
The agenda should include discussions on completed work, work that was not completed, and the next steps to be taken. This level of detail and structure helps to ensure that the Sprint Review is focused and productive.
When presenting completed work, it’s important to ensure that the demonstration is engaging and interactive. To achieve this, consider the following best practices:
By following these best practices, you can ensure that the demonstration of completed work is not only informative but also compelling and impactful for stakeholders.
Effective feedback collection is crucial for continuous improvement:
The Sprint Review Meeting is an important collaborative meeting where team members, engineering leaders, and stakeholders can review previous and discuss key pointers. Below are a few questions that need to be asked during this review meeting:
Use collaborative tools to improve the review process:
Typo is a collaborative tool designed to enhance the efficiency and effectiveness of team meetings, including Sprint Review Meetings. Our sprint analysis feature uses data from Git and issue management tools to provide insights into how your team is working. You can see how long tasks take, how often they’re blocked, and where bottlenecks occur. It allows to track and analyze the team’s progress throughout a sprint and provides valuable insights into work progress, work breakup, team velocity, developer workload, and issue cycle time. This information can help you identify areas for improvement and ensure your team is on track to meet their goals.
Work progress represents the percentage breakdown of issue tickets or story points in the selected sprint according to their current workflow status.
Work breakup represents the percentage breakdown of issue tickets in the current sprint according to their issue type or labels.
Team Velocity represents the average number of completed issue tickets or story points across each sprint.
Developer workload represents the count of issue tickets or story points completed by each developer against the total issue tickets/story points assigned to them in the current sprint.
Issue cycle time represents the average time it takes for an issue ticket to transition from the ‘In Progress’ state to the ‘Completion’ state.
Scope creep is one of the common project management risks. It represents the new project requirements that are added to a project beyond what was originally planned.
Here’s how Typo can be used to improve Sprint Review Meetings:
Typo allows you to create and share detailed agendas with all meeting participants ahead of time. For Sprint Review Meetings, you can outline the key elements such as:
Sharing the agenda in advance ensures everyone knows what to expect and can prepare accordingly.
Typo enhances sprint review meetings by providing real-time collaboration capabilities and comprehensive metrics. Live data access and interactive dashboards ensure everyone has the most current information and can engage in dynamic discussions. Key metrics such as velocity, issue tracking, and cycle time provide valuable insights into team performance and workflow efficiency. This transparency and data-driven approach facilitate informed decision-making, improve accountability, and support continuous improvement, making sprint reviews more productive and collaborative.
Typo makes it easy to collect, organize, and prioritize valuable feedback. Users can utilize feedback forms or surveys integrated within Typo to gather structured feedback from stakeholders. The platform allows for real-time documentation of feedback, ensuring that no valuable insights are lost. Additionally, users can categorize and tag feedback for easier tracking and action planning.
Use Typo’s presentation tools to enhance the demonstration of completed work. Incorporate charts, graphs, and other visual aids to make the progress more understandable and engaging. Use interactive elements to allow stakeholders to explore the new features hands-on.
In Sprint Review Meetings, Typo can be used to drive continuous improvement by analyzing feedback trends, identifying recurring issues or areas for improvement, encouraging team members to reflect on past meetings and suggest enhancements, and implementing data-driven insights to make each Sprint Review more effective than the last.
A well-executed Sprint Review Meeting can significantly enhance your team’s productivity and alignment with stakeholders. By focusing on preparation, effective communication, structured agendas, interactive demos, and continuous improvement, you can transform your Sprint Reviews into a powerful tool for success. Clear goals should be established at the outset of each meeting to provide direction and focus for the team.
Remember, the key is to foster a collaborative environment where valuable feedback is provided and acted upon, driving your team toward continuous improvement and excellence. Integrating tools like Typo can provide the structure and capabilities needed to elevate your Sprint Review Meetings, ensuring they are both efficient and impactful.
Software engineering teams are crucial for the organization. They build high-quality products, gather and analyze requirements, design system architecture and components, and write clean, efficient code. Hence, they are the key drivers of success.
Measuring their success and considering if they are facing any challenges is important. And that’s how Engineering Analytics Tools comes to the rescue. One of the popular tools is LinearB which engineering leaders and CTOs across the globe have widely used.
While this is usually the best choice for the organizations, there might be chances that it doesn’t work for you. Worry not! We’ve curated the top 6 LinearB alternatives that you can take note of when considering engineering analytics tools for your company.
LinearB is a well-known software engineering analytics platform that measures GIT data, tracks DORA metrics, and collects data from other tools. By combining visibility and automation, it enhances operational efficiency and provides a comprehensive view of performance. Its project delivery forecasting and goal-setting features help engineering leaders stay on schedule and monitor team efficiency. LinearB can be integrated with Slack, JIRA, and popular CI/CD tools. However, LinearB has limited features to support the SPACE framework and individual performance insights.
Besides LinearB, there are other leading alternatives as well. Take a look below:
Typo is another popular software engineering intelligence platform that offers SDLC visibility, developer insights, and workflow automation for building high-performing tech teams. It can be seamlessly integrated into the tech tools stack including the GIT version (GitHub, GitLab), issue tracker (Jira, Linear), and CI/CD (Jenkins, CircleCI) tools to ensure a smooth data flow. Typo also offers comprehensive insights into the deployment process through key DORA and other engineering metrics. With its automated code tool, the engineering team can identify code issues and auto-fix them before merging to master.
Jellyfish is a leading GIT tracking tool for tracking metrics by aligning engineering insights with business goals. It analyzes the activities of engineers in a development and management tool and provides a complete understanding of the product. Jellyfish shows the status of every pull request and offers relevant information about the commit that affects the branch. It can be easily integrated with JIRA, Bitbucket, Gitlab, and Confluence.
Swarmia is a popular tool that offers visibility across three crucial areas: business outcome, developer productivity, and developer experience. It provides quantitative insights into the development pipeline. It helps the team identify initiatives falling behind their planned schedule by displaying the impact of unplanned work, scope creep, and technical debt. Swarmia can be integrated with tech tools like source code hosting, issue trackers, and chat systems.
Waydev is a software development analytics platform that uses an agile method for tracking output during the development process. It puts more stress on market-based metrics and gives cost and progress of delivery and key initiatives. Its flexible reporting allows for building complex custom reports. Waydev can be seamlessly integrated with Gitlab, Github, CircleCI, AzureOPS, and other well-known tools.
Pluralsight Flow provides a detailed overview of the development process and helps identify friction and bottlenecks in the development pipeline. It tracks DORA metrics, software development KPIs, and investment insights which allows for aligning engineering efforts with strategic objectives. Pluralsight Flow can be integrated with various manual and automated testing tools such as Azure DevOps, and GitLab.
Sleuth assists development teams in tracking and improving DORA metrics. It provides a complete picture of existing and planned deployments as well as the effect of releases. Sleuth gives teams visibility and actionable insights on efficiency and can be integrated with AWS CloudWatch, Jenkins, JIRA, Slack, and many more.
Software development analytics tools are important for keeping track of project pipelines and measuring developers’ productivity. It allows engineering managers to gain visibility into the dev team performance through in-depth insights and reports.
Take the time to conduct thorough research before selecting any analytics tool. It must align with your team’s needs and specifications, facilitate continuous improvement, and integrate with your existing and forthcoming tech tools.
All the best!
In the dynamic world of software development, where speed and quality are paramount, measuring efficiency is critical. DevOps Research and Assessment (DORA) metrics provide a valuable framework for gauging the performance of software development teams. Two of the most crucial DORA metrics are cycle time and lead time. This blog post will delve into these metrics, explaining their definitions, differences, and significance in optimizing software development processes. To start with, here’s the most simple explanation of the two metrics –
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Lead time refers to the total time it takes to deliver a feature or code change to production, from the moment it’s first conceived as a user story or feature request. In simpler terms, it’s the entire journey of a feature, encompassing various stages like:
Cycle time, on the other hand, focuses specifically on the development stage. It measures the average time it takes for a developer’s code to go from being committed to the codebase to being PR merged. Unlike lead time, which considers the entire delivery pipeline, cycle time is an internal metric that reflects the development team’s efficiency. Here’s a deeper dive into the stages that contribute to cycle time:
Here’s a table summarizing the key distinctions between lead time and cycle time, along with additional pointers to consider for a more nuanced understanding:
Imagine a software development team working on a new feature: allowing users to log in with their social media accounts. Let’s calculate the lead time and cycle time for this feature.
Lead Time = User Story Creation + Estimation + Development & Testing + Code Review & Merge + Deployment & Release Lead Time = 1 Day + 2 Days + 5 Days + 1 Day + 1 Day Lead Time = 10 Days
This considers only the time the development team actively worked on the feature (excluding waiting periods).
Cycle Time = Coding + Code Review Cycle Time = 3 Days + 1 Day Cycle Time = 4 Days
Breakdown:
By monitoring and analyzing both lead time and cycle time, the development team can identify areas for improvement. Reducing lead time could involve streamlining the user story creation or backlog management process. Lowering cycle time might suggest implementing pair programming for faster collaboration or optimizing the code review process.
By understanding the distinct roles of lead time and cycle time, development teams can implement targeted strategies for improvement:
Lead time and cycle time, while distinct concepts, are not mutually exclusive. Optimizing one metric ultimately influences the other. By focusing on lead time reduction strategies, teams can streamline the overall development process, leading to shorter cycle times. Consequently, improving development efficiency through cycle time reduction translates to faster feature delivery, ultimately decreasing lead time. This synergistic relationship highlights the importance of tracking and analyzing both metrics to gain a holistic view of software delivery performance.
Lead time and cycle time are fundamental DORA metrics that provide valuable insights into software development efficiency and customer experience. By understanding their distinctions and implementing targeted improvement strategies, development teams can optimize their workflows and deliver high-quality features faster.
This data-driven approach, empowered by DORA metrics, is crucial for achieving continuous improvement in the fast-paced world of software development. Remember, DORA metrics extend beyond lead time and cycle time. Deployment frequency and change failure rate are additional metrics that offer valuable insights into the software delivery pipeline’s health. By tracking a comprehensive set of DORA metrics, development teams can gain a holistic view of their software delivery performance and identify areas for improvement across the entire value stream.
This empowers teams to:
By evaluating all these DORA metrics holistically, development teams gain a comprehensive understanding of their software development performance. This allows them to identify areas for improvement across the entire delivery pipeline, leading to faster deployments, higher quality software, and ultimately, happier customers.
Software developers have a lot on their plate. Attending too many meetings and that too without any agenda can be overwhelming for them.
The meetings must be with a purpose, help the engineering team to make progress, and provide an opportunity to align their goals, priorities, and expectations.
Below are eight important software engineering meetings you should conduct timely.
There are various types of software engineering meetings. We’ve curated a list of must-have engineering meetings along with a set of metrics.
These metrics serve to provide structure and outcomes for the software engineering meetings. Make sure to ask the right questions with a focus on enhancing team efficiency and align the discussions with measurable metrics.
Such types of meetings happen daily. These are short meetings that typically occur for 15 minutes or less. Daily standup meetings focus on four questions:
It allows software developers to have a clear, concise agenda and focus on the same goal. Moreover, it helps in avoiding duplication of work and prevents wasting time and effort.
These include the questions around inspection, transparency, adaption, and blockers (mentioned above), hence, simplifying the check-in process. It allows team members to understand each others’ updates and track progress over time. This allows standups to remain relevant and productive.
Daily activity promotes a robust, continuous delivery workflow by ensuring the active participation of every engineer in the development process. This metric includes a range of symbols that represent various PR activities of the team’s work such as Commit, Pull Request, PR Merge, Review, and Comment. It further gives valuable information including the type of Git activity, the name and number of the PR, changes in the line of code in this PR, the repository name where this PR lies, and so on.
Work progress helps in understanding what teams are working on and objective measures of their work progress. This allows engineering leaders and developers to better plan for the day, identify blockers in the early stages, and think critically about the progress.
Sprint planning meetings are conducted at the beginning of each sprint. It allows the scrum team to decide what work will they complete in the upcoming iteration, set sprint goals, and align on the next steps. The key purpose of these meetings is for the team to consider how will they approach doing what the product owner has requested.
These plannings are done based on the velocity or capacity and the sprint length.
Sprint goals are the clear, concise objectives the team aims to achieve during the sprint. It helps the team understand what they need to achieve and ensure everyone is on the same page and working towards a common goal.
These are set based on the previous velocity, cycle time, lead time, work-in-progress, and other quality metrics such as defect counts and test coverage.
It represents the Issues/Story Points that were not completed in the sprint and moved to later sprints. Monitoring carry-over items during these meetings allows teams to assess their sprint planning accuracy and execution efficiency. It also enables teams to uncover underlying reasons for incomplete work which further helps identify the root causes to address them effectively.
Developer Workload represents the count of Issue tickets or Story points completed by each developer against the total Issue tickets/Story points assigned to them in the current sprint. Keeping track of developer workload is essential as it helps in informed decision-making, efficient resource management, and successful sprint execution in agile software development.
Planning Accuracy represents the percentage of Tasks Planned versus Tasks Completed within a given time frame. Measuring planning accuracy helps identify discrepancies between planned and completed tasks which further helps in better allocating resources and manpower to tasks. It also enables a better estimate of the time required for tasks, leading to improved time management and more realistic project timelines.
Such types of meetings work very well with sprint planning meetings. These are conducted at every start of the week (Or can be conducted as per the software engineering teams). It helps ensure a smooth process and the next sprint lines up with the team’s requirements to be successful. These meetings help to prioritize tasks, goals, and objectives for the week, what was accomplished in the previous week, and what needs to be done in the upcoming week. This helps align, collaborate, and plan among team members.
Sprint progress helps the team understand how they are progressing toward their sprint goals and whether any adjustments are needed to stay on track. Some of the common metrics for sprint progress include:
Code health provides insights into the overall quality and maintainability of the codebase. Monitoring code health metrics such as code coverage, cyclomatic complexity, and code duplication helps identify areas needing refactoring or improvement. It also offers an opportunity for knowledge sharing and collaboration among team members.
Analyzing pull requests by a team through different data cuts can provide valuable insights into the engineering process, team performance, and potential areas for improvement. Software engineers must follow best dev practices aligned with improvement goals and impact software delivery metrics. Engineering leaders can set specific objectives or targets regarding PR activity for tech teams. It helps to track progress towards these goals, provides insights on performance, and enables alignment with the best practices to make the team more efficient.
Deployment frequency measures how often code is deployed into production per week, taking into account everything from bug fixes and capability improvements to new features. Measuring deployment frequency offers in-depth insights into the efficiency, reliability, and maturity of an engineering team’s development and deployment processes. These insights can be used to optimize workflows, improve team collaboration, and enhance overall productivity.
Performance review meetings help in evaluating engineering works during a specific period. These meetings can be conducted biweekly, monthly, quarterly, and annually. These effective meetings help individual engineers understand their weaknesses, and strengths and improve their work. Engineering managers can provide constructive feedback to them, offer guidance accordingly, and provide growth opportunities.
It measures the percentage of code that is executed by automated tests offers insight into the effectiveness of the testing strategy and helps ensure that critical parts of the codebase are adequately tested. Evaluating code coverage in performance reviews provides insight into the developer’s commitment to producing high-quality, reliable code.
By reviewing PRs in performance review meetings, engineering managers can assess the code quality written by individuals. They can evaluate factors such as adherence to coding standards, best practices, readability, and maintainability. Engineering managers can identify trends and patterns that may indicate areas where developers are struggling to break down tasks effectively.
By measuring developer experience in performance reviews, engineering managers can assess the strengths and weaknesses of a developer’s skill set, and understanding and addressing the aspects can lead to higher productivity, reduced burnout, and increased overall team performance.
Technical meetings are important for software developers and are held throughout the software product life cycle. In such types of meetings, complex software development tasks are carried out, and discuss the best way to solve an issue.
Technical meetings contain three main stages:
The Bugs Rate represents the average number of bugs raised against the total issues completed for a selected time range. This helps assess code quality and identify areas that require improvement. By actively monitoring and managing bug rates, engineering teams can deliver more reliable and robust software solutions that meet or exceed customer expectations.
It represents the number of production incidents that occurred during the selected period. This helps to evaluate the business impact on customers and resolve their issues faster. Tracking incidents allows teams to detect issues early, identify the root causes of problems, and proactively identify trends and patterns.
Time to Build represents the average time taken by all the steps of each deployment to complete in the production environment. Tracking time to build enables teams to optimize build pipelines, reduce build times, and ensure that teams meet service level agreements (SLAs) for deploying changes, maintaining reliability, and meeting customer expectations.
Mean Time to Restore (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week. MTTR reflects the team’s ability to detect, diagnose, and resolve incidents promptly, identifies recurrent or complex issues that require root cause analysis, and allows teams to evaluate the effectiveness of process improvements and incident management practices.
Sprint retrospective meetings play an important role in agile methodology. Usually, the sprints are two weeks long. These are conducted after the review meeting and before the sprint planning meeting. In these types of meetings, the team discusses what went well in the sprint and what could be improved.
In sprint retrospective meetings, the entire team i.e. developers, scrum master, and the product owner are present. This encourages open discussions and exchange learning with each other.
Issue Cycle Time represents the average time it takes for an Issue ticket to transition from the ‘In Progress’ state to the ‘Completion’ state. Tracking issue cycle time is essential as it provides actionable insights for process improvement, planning, and performance monitoring during sprint retrospective meetings. It further helps in pinpointing areas of improvement, identifying areas for workflow optimization, and setting realistic expectations.
Team Velocity represents the average number of completed Issue tickets or Story points across each sprint. It provides valuable insights into the pace at which the team is completing work and delivering value such as how much work is completed, carry over, and if there’s any scope creep. It helps in assessing the team’s productivity and efficiency during sprints, allowing teams to detect and address these issues early on and offer them constructive feedback by continuously tracking them.
It represents the percentage breakdown of Issue tickets or Story points in the selected sprint according to their current workflow status. Tracking work in progress helps software engineering teams gain visibility into the status of individual tasks or stories within the sprint. It also helps identify bottlenecks or blockers in the workflow, streamline workflows, and eliminate unnecessary handoffs.
Throughput is a measure of how many units of information a system can process in a given amount of time. It is about keeping track of how much work is getting done in a specific period. This overall throughput can be measured by
Throughput directly reflects the team’s productivity i.e. whether it is increasing, decreasing, or is constant throughout the sprint. It also evaluates the impact of process changes, sets realistic goals, and fosters a culture of continuous improvement.
These are strategic gatherings that involve the CTO and other key leaders within the tech department. The key purpose of these meetings is to discuss and make decisions on strategic and operations issues related to organizations’ tech initiatives. It allows CTOs and tech leaders to align tech strategy with overall business strategy for setting long-term goals, tech roadmaps, and innovative initiatives.
Besides this, KPIs and other engineering metrics are also reviewed to assess the permanence, measure success, identify blind spots, and make data-driven decisions.
It is the allocation of time, money, and effort across different work categories or projects for a given time. It helps in optimizing resource allocation and drives dev efforts towards areas of maximum business impact. These insights can further be used to evaluate project feasibility, resource requirements, and potential risks. Hence, allocating the engineering team better to drive maximum deliveries.
Measuring DORA metrics is vital for CTO leadership meetings because they provide valuable insights into the effectiveness and efficiency of the software development and delivery processes within the organization. It allows organizations to benchmark their software delivery performance against industry standards and assess how quickly their teams can respond to market changes and deliver value to customers.
DevEx scores directly correlate with developer productivity. A positive DevEx contributes to the achievement of broader business goals, such as increased revenue, market share, and customer satisfaction. Moreover, CTOs and leaders who prioritize DevEx can differentiate their organization as an employer of choice for top technical talent.
In such types of meetings, individuals can have private time with the manager to discuss their challenges, goals, and career progress. They can share their opinion and exchange feedback on various aspects of the work.
Moreover, to create a good working relationship, one-on-one meetings are an essential part of the organization. It allows engineering managers to understand how every team member is feeling at the workplace, setting goals, and discussing concerns regarding their current role.
Metrics are not necessary for one-on-one meetings. While engineering managers can consider the DevEx score and past feedback, their primary focus must be building stronger relationships with their team members, beyond work-related topics.
While working on software development projects is crucial, it is also important to have the right set of meetings to ensure that the team is productive and efficient. These software engineering meetings along with metrics empower teams to make informed decisions, allocate tasks efficiently, meet deadlines, and appropriately allocate resources.
Success in dynamic engineering depends largely on the strength of strategic assumptions. These assumptions serve as guiding principles, influencing decision-making and shaping the trajectory of projects. However, creating robust strategic assumptions requires more than intuition. It demands a comprehensive understanding of the project landscape, potential risks, and future challenges. That’s where engineering benchmarks come in: they are invaluable tools that illuminate the path to success.
Engineering benchmarks serve as signposts along the project development journey. They offer critical insights into industry standards, best practices, and competitors’ performance. By comparing project metrics against these benchmarks, engineering teams understand where they stand in the grand scheme. From efficiency and performance to quality and safety, benchmarking provides a comprehensive framework for evaluation and improvement.
Engineering benchmarks offer many benefits. This includes:
Areas that need improvement can be identified by comparing performance against benchmarks. Hence, enabling targeted efforts to enhance efficiency and effectiveness.
It provides crucial insights for informed decision-making. Therefore, allowing engineering leaders to make data-driven decisions to drive organizational success.
Engineering benchmarks help risk management by highlighting areas where performance deviates significantly from established standards or norms.
Engineering benchmarks provide a baseline against which to measure current performance which helps in effectively tracking progress and monitoring performance metrics before, during, and after implementing changes.
Strategic assumptions are the collaborative groundwork for engineering projects, providing a blueprint for decision-making, resource allocation, and performance evaluation. Whether goal setting, creating project timelines, allocating budgets, or identifying potential risks, strategic assumptions inform every aspect of project planning and execution. With a solid foundation of strategic assumptions, projects can avoid veering off course and failing to achieve their objectives. By working together to build these assumptions, teams can ensure a unified and successful project execution.
No matter how well-planned, every project can encounter flaws and shortcomings that can impede progress or hinder the project’s success. These flaws can take many forms, such as process inefficiencies, performance deficiencies, or resource utilization gaps. Identifying these areas for improvement is essential for ensuring project success and maintaining strategic direction. By recognizing and addressing these gaps early on, engineering teams can take proactive steps to optimize their processes, allocate resources more effectively, and overcome challenges that may arise during project execution, demonstrating problem-solving capabilities in alignment with strategic direction. This can ultimately pave the way for smoother project delivery and better outcomes.
Benchmarking is an essential tool for project management. They enable teams to identify gaps and deficiencies in their projects and develop a roadmap to address them. By analyzing benchmark data, teams can identify improvement areas, set performance targets, and track progress over time.
This continuous improvement can lead to enhanced processes, better quality control, and improved resource utilization. Engineering benchmarks provide valuable and actionable insights that enable teams to make informed decisions and drive tangible results. Access to accurate and reliable benchmark data allows engineering teams to optimize their projects and achieve their goals more effectively.
Incorporating engineering benchmarks in developing strategic assumptions can play a pivotal role in enhancing project planning and execution, fostering strategic alignment within the team. By utilizing benchmark data, the engineering team can effectively validate assumptions, pinpoint potential risks, and make more informed decisions, thereby contributing to strategic planning efforts.
Continuous monitoring and adjustment based on benchmark data help ensure that strategic assumptions remain relevant and effective throughout the project lifecycle, leading to better outcomes. This approach also enables teams to identify deviations early on and take necessary corrective actions before escalating into bigger issues. Moreover, using benchmark data provides teams with a comprehensive understanding of industry standards, best practices, and trends, aiding in strategic planning and alignment.
Integrating engineering benchmarks into the project planning process helps team members make more informed decisions, mitigate risks, and ensure project success while maintaining strategic alignment with organizational goals.
Understanding the key drivers of change is paramount to successfully navigating the ever-shifting landscape of engineering. Technological advancements, market trends, customer satisfaction, and regulatory shifts are among the primary forces reshaping the industry, each exerting a profound influence on project assumptions and outcomes.
Technological progress is the driving force behind innovation in engineering. From materials science breakthroughs to automation and artificial intelligence advancements, emerging technologies can revolutionize project methodologies and outcomes. By staying abreast of these developments and anticipating their implications, engineering teams can leverage technology to their advantage, driving efficiency, enhancing performance, and unlocking new possibilities.
The marketplace is constantly in flux, shaped by consumer preferences, economic conditions, and global events. Understanding market trends is essential for aligning project assumptions with the realities of supply and demand, encompassing a wide range of factors. Whether identifying emerging markets, responding to shifting consumer preferences, or capitalizing on industry trends, engineering teams must conduct proper market research and remain agile and adaptable to thrive in a competitive landscape.
Regulatory frameworks play a critical role in shaping the parameters within which engineering projects operate. Changes in legislation, environmental regulations, and industry standards can have far-reaching implications for project assumptions and requirements. Engineering teams can ensure compliance, mitigate risks, and avoid costly delays or setbacks by staying vigilant and proactive in monitoring regulatory developments.
Engineering projects aim to deliver products, services, or solutions that meet the needs and expectations of end-users. Understanding customer satisfaction provides valuable insights into how well engineering endeavors fulfill these requirements. Moreover, satisfied customers are likely to become loyal advocates for a company’s products or services. Hence, by prioritizing customer satisfaction, engineering org can differentiate their offerings in the market and gain a competitive advantage.
The impact of these key drivers of change on project assumptions cannot be overstated. Failure to anticipate technological shifts, market trends, or regulatory changes can lead to flawed assumptions and misguided strategies. By considering these drivers when formulating strategic assumptions, engineering teams can proactively adapt to evolving circumstances, identify new opportunities, and mitigate potential risks. This proactive approach enhances project resilience and positions teams for success in an ever-changing landscape.
Efficiency is the lifeblood of engineering projects, and benchmarking is a key tool for maximizing efficiency. By comparing project performance against industry standards and best practices, teams can identify opportunities for streamlining processes, reducing waste, and optimizing resource allocation. This, in turn, leads to improved project outcomes and enhanced overall efficiency.
Effectively researching and applying benchmarks is essential for deriving maximum value from benchmarking efforts. Teams should carefully select benchmarks relevant to their project goals and objectives. Additionally, they should develop a systematic approach for collecting, analyzing, and applying benchmark data to inform decision-making and drive project success.
Typo is an intelligent engineering platform that finds real-time bottlenecks in your SDLC, automates code reviews, and measures developer experience. It helps engineering leaders compare the team’s results with healthy benchmarks across industries and drive impactful initiatives. This ensures the most accurate, relevant, and comprehensive benchmarks for the entire customer base.
Average time all merged pull requests have spent in the “Coding”, “Pickup”, “Review” and “Merge” stages of the pipeline.
The average number of deployments per week.
The percentage of deployments that fail in production.
Mean Time to Restore (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week.
If you want to learn more about Typo benchmarks, check out our website now!
Engineering benchmarks are invaluable tools for strengthening strategic assumptions and driving project success. By leveraging benchmark data, teams can identify areas for improvement, set realistic goals, and make informed decisions. Engineering teams can enhance efficiency, mitigate risks, and achieve better outcomes by integrating benchmarking practices into their project workflows. With engineering benchmarks as their guide, the path to success becomes clearer and the journey more rewarding.
Software development culture demands speed and quality. To enhance them and drive business growth, it’s essential to cultivate an environment conducive to innovation and streamline the development process.
One such key factor is development velocity which helps in unlocking optimal performance.
Let’s understand more about this term and why it is important:
Development velocity refers to the amount of work the developers can complete in a specific timeframe. It is the measurement of the rate at which they can deliver business value. In scrum or agile, it is the average number of story points delivered per sprint.
Development velocity is mainly used as a planning tool that helps developers understand how effective they are in deploying high-quality software to end-users.
Development velocity is a strong indicator of whether a business is headed in the right direction. There are various reasons why development velocity is important:
High development velocity leads to an increase in productivity and reduced development time. It further leads to a faster delivery process and reduced time to market which helps in saving cost. Hence, allowing them to maximize the value generated from resources and allocate it to other aspects of business.
High development velocity results in quick delivery of features and updates. Hence, gives the company a competitive edge in the market, responding rapidly to market demands and capturing market opportunities.
Development velocity provides valuable insights into team performance and identifies areas for improvement within the development process. It allows them to analyze velocity trends and implement strategies to optimize their workflow.
Development velocity helps in setting realistic expectations by offering a reliable measure of the team’s capacity to deliver work within the timeframe. It further keeps the expectations grounded in reality and fostering trust and transparency within the development team.
A few common hurdles that may impact the developer’s velocity are:
Measuring development velocity includes quantifying the rate at which developers are delivering value to the project.
Although, various metrics measure development velocity, we have curated a few important metrics. Take a look below:
Cycle Time calculates the time it takes for a task or user story to move from the beginning of the coding task to when it’s been delivered, deployed to production, and made available to users. It provides a granular view of the development process and helps the team identify blindspots and ways to improve them.
Story points measure the number of story points completed over a period of time, typically within a sprint. Tracking the total story points in each iteration or sprint estimates future performance and resource allocation.
User stories measure the velocity in terms of completed user stories. It gives a clear indication of progress and helps in planning future iterations. Moreover, measuring user stories helps in planning and prioritizing their work efforts while maintaining a sustainable pace of delivery.
The Burndown chart tracks the remaining work in a sprint or iteration. Comparing planned work against the actual work progress helps in assessing their velocity and comparing progress to sprint goals. This further helps them in making informed decisions to identify velocity trends and optimize their development process.
Engineering hours track the actual time spent by engineers on specific tasks or user stories. It is a direct measure of effort and helps in estimating future tasks based on historical data. It provides feedback for continuous improvement efforts and enables them to make data-driven decisions and improve performance.
Lead time calculates the time between committing the code and sending it to production. However, it is not a direct metric and it needs to complement other metrics such as cycle time and throughput. It helps in understanding how quickly the development team is able to respond to new work and deliver value.
Developers are important assets of software development companies. When they are unhappy, this leads to reduced productivity and morale. This further lowers code quality and creates hurdles in collaboration and teamwork. As a result, this negatively affects the development velocity.
Hence, the first and most crucial way is to create a positive work environment for developers. Below are a few ways how you can build a positive developer experience for them:
Encouraging a culture of experimentation and continuous learning leads to innovation and the adoption of more efficient practices. Let your developers, experiment, make mistakes and try again. Ensure that you acknowledge their efforts and celebrate their successes.
Unrealistic deadlines can cause burnout, poor code quality work, and negligence in PR review. Always involve your development team while setting deadlines. When set right, it can help them plan and prioritize their tasks. Ensure that you give buffer time to them to manage roadblocks and unexpected bugs as well as other priorities.
Regular communication among team leaders and developers lets them share important information on a priority basis. It allows them to effectively get their work done since they are communicating their progress and blockers while simultaneously moving on with their tasks.
Knowledge sharing and collaboration are important. This can be through pair programming and collaborating with other developers as it allows them to work on more complex problems and code together in parallel. It also results in effective communication as well as accountability for each other’s work.
An increase in technical debt negatively impacts the development velocity. When teams take shortcuts, they have to spend extra time and effort on fixing bugs and other issues. It also leads to improper planning and documentation which further slows down the development process.
Below are a few ways how developers can minimize technical debt:
The automated testing process minimizes the risk of errors in the future and identifies defects in code quickly. Further, it increases the efficiency of engineers. Hence, giving them more time to solve problems that need human interference.
Code reviews in routine allow the team to handle technical debt in the long run. As it helps in constant error checking and catching potential issues which enhance code quality.
Refactoring involves making changes to the codebase without altering its external behavior. It is an ongoing process that is performed regularly throughout the software development life cycle.
Always listen to your engineers. They are the ones who are well aware of ongoing development and working closely with a database and developing the applications. Listen to what they have to say and take their suggestions and opinions.
Agile methodologies such as Scrum and Kanban offer a framework to manage software development projects flexibly and seamlessly. This is because the framework breaks down projects into smaller, manageable increments. Hence, allowing them to focus on delivering small pieces of functions more quickly. It also enables developers to receive feedback quickly and have constant communication with the team members.
The agile methodology also prioritizes work based on business value, customer needs and dependencies to streamline developers’ efforts and maintain consistent progress.
One of the best ways the software development process works efficiently is when everyone’s goals are aligned. If not, it could lead to being out of sync and stuck in a bottleneck situation. Aligning objectives with other teams fosters collaboration reduces duplication of efforts, and ensures that everyone is working towards the same goal.
Moreover, it minimizes the conflicts and dependencies between teams enabling faster decision making and problem-solving. Hence, development teams should regularly communicate, coordinate, and align with priorities to ensure a shared understanding of objectives and vision.
Right engineering tools and technologies can help in increasing productivity and development velocity. Organizations that have tools for continuous integration and deployment, communication, collaboration, planning, and development are likely more innovative than the companies that don’t use them.
There are many tools available in the market. Below are key factors that the engineering team should keep in mind while choosing any engineering tool:
As mentioned above, empowering your development team to use the right tools is crucial. Typo is one such intelligent engineering platform that is used for gaining visibility, removing blockers, and maximizing developer effectiveness.
DevOps is a set of practices that promotes collaboration and communication between software development and IT operations teams. It has become a crucial part of the modern software development landscape.
Within DevOps, DORA metrics (DevOps Research and Assessment) are essential in evaluating and improving performance. This guide is aimed at providing a comprehensive overview of the best DORA metrics trackers for 2024. It offers insights into their features and benefits to help organizations optimize their DevOps practices.
DORA metrics serve as a compass for evaluating software development performance. Four key metrics include deployment frequency, change lead time, change failure rate, and mean time to recovery (MTTR).
Deployment frequency measures how often code is deployed to production.
It is essential to measure the time taken from code creation to deployment, known as change lead time. This metric helps to evaluate the efficiency of the development pipeline.
Change failure rate measures a team’s ability to deliver reliable code. By analyzing the rate of unsuccessful changes, teams can identify areas for improvement in their development and deployment processes.
Mean Time to Recovery (MTTR) is a metric that measures the amount of time it takes a team to recover from failures.
Typo establishes itself as a frontrunner among DORA metrics trackers. It is an intelligent engineering management platform used for gaining visibility, removing blockers, and maximizing developer effectiveness. Typo’s user-friendly interface and cutting-edge capabilities set it apart in the competitive landscape.
In direct comparison to alternative trackers, Typo distinguishes itself through its intuitive design and robust functionality for engineering teams. While other options may excel in certain aspects, Typo strikes a balance by delivering a holistic solution that caters to a broad spectrum of DevOps requirements.
Typo’s prominence in the field is underscored by its technical capabilities and commitment to providing a user-centric experience. This blend of innovation, adaptability, and user-friendliness positions Typo as the leading choice for organizations seeking to elevate their DORA metrics tracking in 2024.
LinearB introduces a collaborative approach to DORA metrics, emphasizing features that enhance teamwork and overall efficiency. Real-world examples demonstrate how collaboration can significantly impact DevOps performance, making LinearB a standout choice for organizations prioritizing team synergy and collaboration.
LinearB’s focus on collaboration shared visibility, and real-time interactions positions it as a tool that tracks metrics and actively contributes to improved team dynamics and overall DevOps performance.
Jellyfish excels in adapting to diverse DevOps environments, offering customizable options and seamless integration capabilities. Whether deployed in the cloud or on-premise setups, Jellyfish ensures a smooth and adaptable tracking experience for DevOps teams seeking flexibility in their metrics monitoring.
Jellyfish’s success is further showcased through real-world implementations, highlighting its flexibility and ability to meet the unique requirements of different DevOps environments. Its adaptability positions Jellyfish as a reliable and versatile choice for organizations navigating the complexities of modern software development.
Faros stands out as a robust DORA metrics tracker, emphasizing precision and effectiveness in measurement. Its feature set is specifically designed to ensure the accurate evaluation of critical metrics such as deployment frequency, lead time for changes, change failure rate, and mean time to recover. Faros’ impact extends to industries with stringent requirements, notably finance, and healthcare, where precise metrics are imperative for success.
Faros, focusing on precision and industry-specific solutions, positions itself as an indispensable tool for organizations that prioritize accuracy and reliability in their DORA metrics tracking.
Haystack simplifies the complexity associated with DORA metrics tracking through its user-friendly features. The efficiency of Haystack is evident in its customizable dashboards and streamlined workflows, offering a solution tailored for teams seeking simplicity and efficiency in their DevOps practices.
Success stories further underscore the positive impact Haystack has on organizations navigating complex DevOps landscapes. The combination of user-friendly features and efficient workflows positions Haystack as an excellent choice for teams seeking a straightforward yet powerful DORA metrics tracking solution.
Choosing the right tool can be overwhelming so here are some factors for you to consider Typo as the leading choice:
Typo’s automated code review tool not only enables developers to catch issues related to code maintainability, readability, and potential bugs but also can detect code smells. It identifies issues in the code and auto-fixes them before you merge to master.
In comparison to other trackers, Typo offers a 360 view of your developer experience. It helps in identifying the key priority areas affecting developer productivity and well-being as well as benchmark performance by comparing results against relevant industries and team sizes.
Typo’s commitment to staying ahead in the rapidly evolving DevOps space is evident through its customer support as the majority of the end-users’ queries are solved within 24-48 hours.
If you’re looking for a DORA metrics tracker that can help you optimize DevOps performance, Typo is the ideal solution for you. With its unparalleled features, intuitive design, and ongoing commitment to innovation, Typo is the perfect choice for software development teams seeking a solution that seamlessly integrates with their CI/CD pipelines, offers customizable dashboards, and provides real-time insights.
Typo not only addresses common pain points but also offers a comprehensive solution that can help you achieve your organizational goals. It’s easy to get started with Typo, and we’ll guide you through the process step-by-step to ensure that you can harness its full potential for your organization’s success.
So, if you’re ready to take your DevOps performance to the next level..
In the constantly changing world of software development, it is crucial to have reliable metrics to measure performance. This guide provides a detailed overview of DORA (DevOps Research and Assessment) metrics, explaining their importance in assessing the effectiveness, efficiency, and dependability of software development processes.
DORA metrics serve as a compass for evaluating software development performance. This guide covers deployment frequency, change lead time, change failure rate, and mean time to recovery (MTTR).
Let’s explore the key DORA metrics that are crucial for assessing the efficiency and reliability of software development practices. These metrics provide valuable insights into a team's agility, adaptability, and resilience to change.
Deployment Frequency measures how often code is deployed to production. The frequency of code deployment reflects how agile, adaptable, and efficient the team is in delivering software solutions. This metric, explained in our guide, provides valuable insights into the team's ability to respond to changes, enabling strategic adjustments in development practices.
It is essential to measure the time taken from code creation to deployment, which is known as change lead time. This metric helps to evaluate the efficiency of the development pipeline, emphasizing the importance of quick transitions from code creation to deployment. Our guide provides a detailed analysis of how optimizing change lead time can significantly improve overall development practices.
Change failure rate measures a team's ability to deliver reliable code. By analyzing the rate of unsuccessful changes, teams can identify areas for improvement in their development and deployment processes. This guide provides detailed insights on interpreting and leveraging change failure rate to enhance code quality and reliability.
Mean Time to Recovery (MTTR) is a metric that measures the amount of time it takes a team to recover from failures. This metric is important because it helps gauge a team's resilience and recovery capabilities, which are crucial for maintaining a stable and reliable software environment. Our guide will explore how understanding and optimizing MTTR can contribute to a more efficient and resilient development process.
Below are the performance metrics categorized in
for 4 metrics –
Utilizing DORA (DevOps Research and Assessment) metrics goes beyond just understanding individual metrics. It involves delving into the practical application of DORA metrics that are specifically tailored for DevOps teams. By actively tracking and reporting on these metrics over time, teams can gain actionable insights, identify trends, and patterns, and pinpoint areas for continuous improvement. Furthermore, by aligning DORA metrics with business value, organizations can ensure that their DevOps efforts contribute directly to strategic objectives and overall success.
The guide recommends that engineering teams begin by assessing their current DORA metric values to establish a baseline. This baseline is a reference point for measuring progress and identifying deviations over time. By understanding their deployment frequency, change lead time, change failure rate, and MTTR, teams can set realistic improvement goals specific to their needs.
Consistently monitoring DORA (DevOps Research and Assessment) metrics helps software teams detect patterns and trends in their development and deployment processes. This guide provides valuable insights into how analyzing deployment frequency trends can reveal the team's ability to adapt to changing requirements while assessing change lead time trends can offer a glimpse into the workflow's efficiency. By identifying patterns in change failure rates, teams can pinpoint areas that need improvement, enhancing the overall software quality and reliability.
Using DORA metrics is a way for DevOps teams to commit to continuously improving their processes and track progress. The guide promotes an iterative approach, encouraging teams to use metrics to develop targeted strategies for improvement. By optimizing deployment pipelines, streamlining workflows, or improving recovery mechanisms, DORA metrics can help drive positive changes in the development lifecycle.
The DORA metrics have practical implications in promoting cross-functional cooperation among DevOps teams. By jointly monitoring and analyzing metrics, teams can eliminate silos and strive towards common goals. This collaborative approach improves communication, speeds up decision-making, and ensures that everyone is working towards achieving shared objectives.
DORA metrics form the basis for establishing a culture of feedback-driven development within DevOps teams. By consistently monitoring metrics and analyzing performance data, teams can receive timely feedback, allowing them to quickly adjust to changing circumstances. This ongoing feedback loop fosters a dynamic development environment where real-time insights guide continuous improvements. Additionally, aligning DORA metrics with operational performance metrics enhances the overall understanding of system behavior, promoting more effective decision-making and streamlined operational processes.
DORA metrics isn’t just a mere theory to support DevOps but it has practical applications to elevate how your team works. Here are some of them:
Efficiency and speed are crucial in software development. The guide explores methods to measure deployment frequency, which reveals how frequently code is deployed to production. This measurement demonstrates the team's agility and ability to adapt quickly to changing requirements. This emphasizes a culture of continuous delivery.
Quality assurance plays a crucial role in software development, and the guide explains how DORA metrics help in evaluating and ensuring code quality. By analyzing the change failure rate, teams can determine the dependability of their code modifications. This helps them recognize areas that need improvement, promoting a culture of delivering top-notch software.
Reliability is crucial for the success of software applications. This guide provides insights into Mean Time to Recovery (MTTR), a key metric for measuring a team's resilience and recovery capabilities. Understanding and optimizing MTTR contributes to a more reliable development process by ensuring prompt responses to failures and minimizing downtime.
Benchmarks play a crucial role in measuring the performance of a team. By comparing their performance against both the industry standards and their own team-specific goals, software development teams can identify areas that need improvement. This iterative process allows for continuous execution enhancement, which aligns with the principles of continuous improvement in DevOps practices.
Value Stream Management is a crucial application of DORA metrics. It provides development teams with insights into their software delivery processes and helps them optimize for efficiency and business value. It enables quick decision-making, rapid response to issues, and the ability to adapt to changing requirements or market conditions.
Implementing DORA metrics brings about a transformative shift in the software development process, but it is not without its challenges. Let’s explore the potential hurdles faced by teams adopting DORA metrics and provide insightful solutions to navigate these challenges effectively.
One of the main challenges faced is the reluctance of the development team to change. The guide explores ways to overcome this resistance, emphasizing the importance of clear communication and highlighting the long-term advantages that DORA metrics bring to the development process. By encouraging a culture of flexibility, teams can effectively shift to a DORA-centric approach.
To effectively implement DORA metrics, it is important to have a clear view of data across the development pipeline. The guide provides solutions for overcoming challenges related to data visibility, such as the use of integrated tools and platforms that offer real-time insights into deployment frequency, change lead time, change failure rate, and MTTR. This ensures that teams are equipped with the necessary information to make informed decisions.
Organizational silos can hinder the smooth integration of DORA metrics into the software development workflow. In this guide, we explore different strategies that can be used to break down these silos and promote cross-functional collaboration. By aligning the goals of different teams and working together towards a unified approach, organizations can fully leverage the benefits of DORA metrics in improving software development performance.
Ensuring the success of DORA implementation relies heavily on selecting and defining relevant metrics. The guide emphasizes the importance of aligning the chosen metrics with organizational goals and objectives to overcome the challenge of ensuring metric relevance. By tailoring metrics to specific needs, teams can extract meaningful insights for continuous improvement.
Implementing DORA metrics across multiple teams and projects can be a challenge for larger organizations. To address this challenge, the guide offers strategies for scaling the implementation. These strategies include the adoption of standardized processes, automated tools, and consistent communication channels. By doing so, organizations can achieve a harmonized approach to DORA metrics implementation.
Anticipating future trends in DORA metrics is essential for staying ahead in the dynamic landscape of software development. Here are some of them:
As the software development landscape continues to evolve, there is a growing trend towards integrating DORA metrics with artificial intelligence (AI) and machine learning (ML) technologies. These technologies can enhance predictive analytics, enabling teams to proactively identify potential bottlenecks, optimize workflows, and predict failure rates. This integration empowers organizations to make data-driven decisions, ultimately improving the overall efficiency and reliability of the development process.
DORA metrics are expected to expand their coverage beyond the traditional four key metrics. This expansion may include metrics related to security, collaboration, and user experience, allowing teams to holistically assess the impact of their development practices on various aspects of software delivery.
Future trends in DORA metrics emphasize the importance of continuous feedback loops and iterative improvement. Organizations are increasingly adopting a feedback-driven culture, leveraging DORA metrics to provide timely insights into the development process. This iterative approach enables teams to identify areas for improvement, implement changes, and measure the impact, fostering a cycle of continuous enhancement.
Advancements in data visualization and reporting tools are shaping the future of DORA metrics. Organizations are investing in enhanced visualization techniques to make complex metric data more accessible and actionable. Improved reporting capabilities enable teams to communicate performance insights effectively, facilitating informed decision-making at all levels of the organization.
DORA metrics in software development serve as both evaluative tools and innovators, playing a crucial role in enhancing Developer Productivity and guiding engineering leaders. DevOps practices rely on deployment frequency, change lead time, change failure rate, and MTTR insights gained from DORA metrics. They create a culture of improvement, collaboration, and feedback-driven development. Future integration with AI, expanded metric coverage, and enhanced visualization herald a shift in navigating the complex landscape. Metrics have transformative power in guiding DevOps teams towards resilience, efficiency, and success in a constantly evolving technological landscape.
The Mean Time to Recover (MTTR) is a crucial measurement within DORA (DevOps Research and Assessment) metrics. It provides insights into how fast an organization can recover from disruptions. In this blog post, we will discuss the importance of MTTR in DevOps and its role in improving system reliability while reducing downtime.
MTTR, which stands for Mean Time to Recover, is a valuable metric that calculates the average duration taken by a system or application to recover from a failure or incident. It is an essential component of the DORA metrics and concentrates on determining the efficiency and effectiveness of an organization's incident response and resolution procedures.
It is a useful metric to measure for various reasons:
Efficient incident resolution is crucial for maintaining seamless operations and meeting user expectations. MTTR plays a pivotal role in the following aspects:
MTTR is directly related to an organization's ability to respond quickly to incidents. A lower MTTR indicates a DevOps team that is more agile and responsive and can promptly address issues.
Organizations' key goal is to minimize downtime. MTTR quantifies the time it takes to restore normalcy, reducing the impact on users and businesses. software delivery software development software development
A fast recovery time leads to a better user experience. Users appreciate services that have minimal disruptions, and a low MTTR shows a commitment to user satisfaction.
It is a key metric that encourages DevOps teams to build more robust systems. Besides this, it is completely different from the other three DORA metrics.
MTTR metric measures the severity of the impact. It indicates how quickly DevOps can acknowledge unplanned breakdowns and repair them, providing valuable insights into incident response time.
To calculate this, add up the total downtime and divide it by the total number of incidents that occurred within a particular period. For example, the time spent on unplanned maintenance is 60 hours. The total number of incidents that occurred is 10 times. Hence, the mean time to recover would be 6 hours.
The response time should be as short as possible. 24 hours is considered to be a good rule of thumb.
High Mttr means the product will be unavailable to end users for a longer time period. This further results in lost revenue, productivity, and customer dissatisfaction. DevOps needs to ensure continuous monitoring and prioritize recovery when a failure occurs.
With Typo, you can improve dev efficiency with an inbuilt DORA metrics dashboard.
Downtime can be detrimental, impacting revenue and customer trust. MTTR measures the time taken to recover from a failure. A high MTTR indicates inefficiencies in issue identification and resolution. Investing in automation, refining monitoring systems, and bolstering incident response protocols minimizes downtime, ensuring uninterrupted services.
Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.
A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.
MTTR is more than just a metric; it reflects engineering teams' commitment to resilience, customer satisfaction, and continuous improvement. A low MTTR signifies:
Having an efficient incident response process indicates a well-structured incident management system capable of handling diverse challenges.
Proactively identifying and addressing underlying issues can prevent recurrent incidents and result in low MTTR values.
Trust plays a crucial role in service-oriented industries. A low mean time to resolve (MTTR) builds trust among users, stakeholders, and customers by showcasing reliability and a commitment to service quality.
Efficient incident recovery ensures prompt resolution without workflow disruption, leading to operational efficiency.
User satisfaction is directly proportional to the reliability of the system. A low Mean Time To Repair (MTTR) results in a positive user experience, which enhances overall satisfaction.
Minimizing downtime is crucial to maintain business continuity and ensure critical systems are consistently available.
Optimizing MTTR involves implementing strategic practices to enhance incident response and recovery. Key strategies include:
Leveraging automation for incident detection, diagnosis, and recovery can significantly reduce manual intervention, accelerating recovery times.
Fostering collaboration among development, operations, and support teams ensures a unified response to incidents, improving overall efficiency.
Implement continuous monitoring for real-time issue detection and resolution. Monitoring tools provide insights into system health, enabling proactive incident management.
Investing in team members' training and skill development can improve incident efficiency and reduce MTTR.
Establishing a dedicated incident response team with defined roles and responsibilities contributes to effective incident resolution. This further enhances overall incident response capabilities.
The Mean Time to Recover (MTTR) is a crucial measure in the DORA framework that reflects engineering teams' ability to bounce back from incidents, work efficiently, and provide dependable services. To improve incident response times, minimize downtime, and contribute to their overall success, organizations should recognize the importance of MTTR, implement strategic improvements, and foster a culture of continuous enhancement. Key Performance Indicator considerations play a pivotal role in this process.
For teams seeking to stay ahead in terms of productivity and workflow efficiency, Typo offers a compelling solution. Uncover the complete spectrum of Typo's capabilities designed to enhance your team's productivity and streamline workflows. Whether you're aiming to optimize work processes or foster better collaboration, Typo's impactful features, aligned with Key Performance Indicator objectives, provide the tools you need. Embrace heightened productivity by unlocking the full potential of Typo for your team's success today.
DevOps Research and Assessment (DORA) metrics are a compass for engineering teams striving to optimize their development and operations processes. This detailed guide will explore each facet of measuring DORA metrics to empower your journey toward DevOps excellence.
Given below are four key DORA metrics that help in measuring software delivery performance:
Deployment frequency is a key indicator of agility and efficiency. Regular deployments signify a streamlined pipeline, allowing teams to deliver features and updates faster.It is important to measure Deployment Frequency for various reasons:
This metric measures the time it takes for code changes to move from inception to deployment. A shorter lead time indicates a responsive development cycle and a more efficient workflow.It is important to measure Lead Time for Changes for various reasons:
The mean time to recovery reflects how quickly a team can bounce back from incidents or failures. A lower mean time to recovery is synonymous with a resilient system capable of handling challenges effectively.
It is important to Mean Time to Recovery for various reasons:
Change failure rate gauges the percentage of changes that fail. A lower failure rate indicates a stable and reliable application, minimizing disruptions caused by failed changes.
Understanding the nuanced significance of each metric is essential for making informed decisions about the efficacy of your DevOps processes.
It is important to measure the Change Failure Rate for various reasons:
Efficient measurement of DORA metrics, crucial for optimizing deployment processes and ensuring the success of your DevOps team, requires the right tools, and one such tool that stands out is Typo.
Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics, providing an alternative and efficient solution for development teams seeking precision in their DevOps performance measurement.
Typo is a software delivery management platform used for gaining visibility, removing blockers, and maximizing developer effectiveness. Typo integrates with your tech stacks like Git providers, issue trackers, CI/CD, and incident tools to identify key blockers in the dev processes and stay aligned with business goals.
Visit our website https://typoapp.io/dora-metrics and sign up using your preferred version control system (Github, Gitlab, or Bitbucket).
Follow the onboarding process detailed on the website and connect your git, issue tracker, and Slack.
Based on the number of members and repositories, Typo automatically syncs with your git and issue tracker data and shows insights within a few minutes.
Lastly, set your metrics configuration specific to your development processes as mentioned below:
For setting up Deployment Frequency, you need to provide us with the details of how your team identifies deployments with other details like the name of the branches- Main/Master/Production you use for production deployment.
If there is a process you follow to detect deployment failures, for example, if you use labels like hotfix, rollbacks, etc for identifying PRs/tasks created to fix failed deployments, Typo will read those labels accordingly and provide insights based on your failure rate and the time to restore from those failures.
Cycle time is automatically configured when setting up the DORA metrics dashboard. Typo Cycle Time takes into account pull requests that are still in progress. To calculate the Cycle Time for open pull requests, they are assumed to be closed immediately.
In the rapidly changing world of DevOps, attaining excellence is not an ultimate objective but an ongoing and cyclical process. To accomplish this, measuring DORA (DevOps Research and Assessment) metrics becomes a vital aspect of this journey, creating a continuous improvement loop that covers every stage of your DevOps practices.
The process of measuring DORA metrics is not simply a matter of ticking boxes or crunching numbers. It is about comprehending the narrative behind these metrics and what they reveal about your DevOps procedures. The cycle starts by recognizing that each metric represents your team's effectiveness, dependability, and flexibility.
Consistency is key to making progress. Establish a routine for reviewing DORA metrics – this could be weekly, monthly, or by your development cycles. Delve into the data, and analyze the trends, patterns, and outliers. Determine what is going well and where there is potential for improvement.
During the analysis phase, you can get a comprehensive view of your DevOps performance. This will help you identify the areas where your team is doing well and the areas that need improvement. The purpose of this exercise is not to assign blame but to gain a better understanding of your DevOps ecosystem's dynamics.
After gaining insights from analyzing DORA metrics, implementing iterative changes involves fine-tuning the engine rather than making drastic overhauls.
Continuous improvement is fostered by a culture of experimentation. It's important to motivate your team to innovate and try out new approaches, such as adjusting deployment frequencies, optimizing lead times, or refining recovery processes. Each experiment contributes to the development of your DevOps practices and helps you evolve and improve over time.
Rather than viewing failure as an outcome, see it as an opportunity to gain knowledge. Embrace the mindset of learning from your failures. If a change doesn't produce the desired results, use it as a chance to gather information and enhance your strategies. Your failures can serve as a foundation for creating a stronger DevOps framework.
DevOps is a constantly evolving practice that is influenced by various factors like technology advancements, industry trends, and organizational changes. Continuous improvement requires staying up-to-date with these dynamics and adapting DevOps practices accordingly. It is important to be agile in response to change.
It's important to create feedback loops within your DevOps team. Regularly seek input from team members involved in different stages of the pipeline. Their insights provide a holistic view of the process and encourage a culture of collaborative improvement.
Acknowledge and celebrate achievements, big or small. Recognize the positive impact of implemented changes on DORA metrics. This boosts morale and reinforces a culture of continuous improvement.
To optimize DevOps practices and enhance organizational performance, organizations must master key metrics—Deployment Frequency, Lead Time for Changes, Mean Time to Recovery, and Change Failure Rate. Specialized tools like Typo simplify the measurement process, while GitLab's documentation aligns practices with industry standards. Successful DevOps teams prioritize continuous improvement through regular analysis, iterative adjustments, and adaptive responses. By using DORA metrics and committing to improvement, organizations can continuously elevate their performance.
Gain valuable insights and empower your engineering managers with Typo's robust capabilities.
In the rapidly evolving world of DevOps, it is essential to comprehend and improve your development and delivery workflows. To evaluate and enhance the efficiency of these workflows, the DevOps Research and Assessment (DORA) metrics serve as a crucial tool.
This blog, specifically designed for Typo, offers a comprehensive guide on creating a DORA metrics dashboard that will help you optimize your DevOps performance.
The DORA metrics consist of four key metrics:
Deployment frequency measures the frequency of deployment of code to production or releases to end-users in a given time frame.
This metric measures the time between a commit being made and that commit making it to production.
Change failure rate measures the proportion of deployment to production that results in degraded services.
This metric is also known as the mean time to restore. It measures the time required to solve the incident i.e. service incident or defect impacting end-users.
These metrics provide valuable insights into the performance of your software development pipeline. By creating a well-designed dashboard, you can visualize these metrics and make informed decisions to improve your development process continuously.
Before you choose a platform for your DORA Metrics Dashboard, it's important to first define clear and measurable objectives. Consider the Key Performance Indicators (KPIs) that align with your organizational goals. Whether it's improving deployment speed, reducing failure rates, or enhancing overall efficiency, having a well-defined set of objectives will help guide your implementation of the dashboard.
When searching for a platform, it's important to consider your goals and requirements. Look for a platform that is easy to integrate, scalable, and customizable. Different platforms, such as Typo, have unique features, so choose the one that best suits your organization's needs and preferences.
Gain a deeper understanding of the DevOps Research and Assessment (DORA) metrics by exploring the nuances of Deployment Frequency, Lead Time, Change Failure Rate, and MTTR. Then, connect each of these metrics with your organization's DevOps goals to have a comprehensive understanding of how they contribute towards improving overall performance and efficiency.
After choosing a platform, it's important to follow specific guidelines to properly configure your dashboard. Customize the widgets to accurately represent important metrics and personalize the layout to create a clear and intuitive visualization of your data. This ensures that your team can easily interpret the insights provided by the dashboard and take appropriate actions.
To ensure the accuracy and reliability of your DORA Metrics, it is important to establish strong data collection mechanisms. Configure your dashboard to collect real-time data from relevant sources, so that the metrics reflect the current state of your DevOps processes. This step is crucial for making informed decisions based on up-to-date information.
To optimize the performance of your DORA Metrics Dashboard, you can integrate automation tools. By utilizing automation for data collection, analysis, and reporting processes, you can streamline routine tasks. This will free up your team's time and allow them to focus on making strategic decisions and improvements, instead of spending time on manual data handling.
To get the most out of your well-configured DORA Metrics Dashboard, use the insights gained to identify bottlenecks, streamline processes, and improve overall DevOps efficiency. Analyze the dashboard data regularly to drive continuous improvement initiatives and make informed decisions that will positively impact your software development lifecycle.
Aggregating diverse data sources into a unified dashboard is one of the biggest hurdles while building the DORA metrics dashboard.
For example, if the metrics to be calculated is 'Lead time for changes' and sources include a version control system in GIT, Issue tracking in Jira, and a Build server in Jenkins. The timestamps recorded in Git, Jira, and Jenkins may not be synchronized or standardized and they may capture data at different levels of granularity.
Another challenge is whether the dashboard effectively communicates the insights derived from the metrics.
Suppose, you want to get visualized insights for deployment frequency. You choose a line chart for the same. However, if the frequency is too high, the chart might become cluttered and difficult to interpret. Moreover, displaying deployment frequency without additional information can lead to misinterpretation of the metric.
Teams may fear that the DORA dashboard will be used for blame rather than the improvement. Moreover, if there's a lack of trust in the organization, they question the motives behind implementing metrics and doubt the fairness of the process.
Typo, as a dynamic platform, provides a user-friendly interface and robust features tailored for DevOps excellence.
Leveraging Typo for your DORA Metrics Dashboard offers several advantages:
It integrates with key DevOps tools, ensuring a smooth data flow for accurate metric representation.
It allows for easy customization of widgets, aligning the dashboard precisely with your organization's unique metrics and objectives.
Typo's automation features streamline data collection and reporting, reducing manual efforts and ensuring real-time, accurate insights.
It facilitates collaboration among team members, allowing them to collectively interpret and act upon dashboard insights, fostering a culture of continuous improvement.
It is designed to scale with your organization's growth, accommodating evolving needs and ensuring the longevity of your DevOps initiatives.
When you opt for Typo as your preferred platform, you enable your team to fully utilize the DORA metrics. This drives efficiency, innovation, and excellence throughout your DevOps journey. Make the most of Typo to take your DevOps practices to the next level and stay ahead in the competitive software development landscape of today.
DORA metrics dashboard plays a crucial role in optimizing DevOps performance.
Building the dashboard with Typo provides various benefits such as tailored integration and customization. To know more about it, book your demo today!
DORA Metrics assesses and enhances software delivery performance. Strategic considerations are necessary to identify areas of improvement, reduce time-to-market, and improve software quality. Effective utilization of DORA Metrics can drive positive organizational changes and achieve software delivery goals.
In 2015, The DORA team was founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren to evaluate and improve software development practices. The aim was to enhance the understanding of how organizations can deliver reliable and high-quality software faster.
To achieve success in the field of software development, it is crucial to possess a comprehensive understanding of DORA metrics. DORA, which stands for DevOps Research and Assessment, has identified four key DORA metrics critical in measuring and enhancing software development processes.
Mastering these metrics is fundamental for accurately interpreting the performance of software development processes and identifying areas for improvement. By analyzing these metrics, DevOps teams can identify bottlenecks and inefficiencies, streamline their processes, and ultimately deliver reliable and high-quality software faster.
The DORA (DevOps Research and Assessment) metrics are widely used to measure and improve software delivery performance. However, to make the most of these metrics, it is important to tailor them to align with specific organizational goals. By doing so, organizations can ensure that their improvement strategy is focused and impactful, addressing unique business needs.
Customizing DORA metrics requires a thorough understanding of the organization's goals and objectives, as well as its current software delivery processes. This may involve identifying the key performance indicators (KPIs) that are most relevant to the organization's specific goals, such as faster time-to-market or improved quality.
Once these KPIs have been identified, the organization can use DORA metrics data to track and measure its performance in these areas. By regularly monitoring these metrics, the organization can identify areas for improvement and implement targeted strategies to address them.
Consistency in measuring and monitoring DevOps Research and Assessment (DORA) metrics over time is essential for establishing a reliable feedback loop. This feedback loop enables organizations to make data-driven decisions, identify areas of improvement, and continuously enhance their software delivery processes. By measuring and monitoring DORA metrics consistently, organizations can gain valuable insights into their software delivery performance and identify areas that require attention. This, in turn, allows the organization to make informed decisions based on actual data, rather than intuition or guesswork. Ultimately, this approach helps organizations to optimize their software delivery pipelines and improve overall efficiency, quality, and customer satisfaction.
Using the DORA metrics as a collaborative tool can greatly benefit organizations by fostering shared responsibility between development and operations teams. This approach helps break down silos and enhances overall performance by improving communication and increasing transparency.
By leveraging DORA metrics, engineering teams can gain valuable insights into their software delivery processes and identify areas for improvement. These metrics can also help teams measure the impact of changes and track progress over time. Ultimately, using DORA metrics as a collaborative tool can lead to more efficient and effective software delivery and better alignment between development and operations teams.
Prioritizing the reduction of lead time involves streamlining the processes involved in the production and delivery of goods or services, thereby enhancing business value. By minimizing the time taken to complete each step, businesses can achieve faster delivery cycles, which is essential in today's competitive market.
This approach also enables organizations to respond more quickly and effectively to the evolving needs of customers. By reducing lead time, businesses can improve their overall efficiency and productivity, resulting in greater customer satisfaction and loyalty. Therefore, businesses need to prioritize the reduction of lead time if they want to achieve operational excellence and stay ahead of the curve.
When it comes to implementing DORA metrics, it's important to adopt an iterative approach that prioritizes adaptability and continuous improvement. By doing so, organizations can remain agile and responsive to the ever-changing technological landscape.
Iterative processes involve breaking down a complex implementation into smaller, more manageable stages. This allows teams to test and refine each stage before moving onto the next, which ultimately leads to a more robust and effective implementation.
Furthermore, an iterative approach encourages collaboration and communication between team members, which can help to identify potential issues early on and resolve them before they become major obstacles. In summary, viewing DORA metrics implementation as an iterative process is a smart way to ensure success and facilitate growth in a rapidly changing environment.
Recognizing and acknowledging the progress made in the DORA metrics is an effective way to promote a culture of continuous improvement within the organization. It not only helps boost the morale and motivation of the team but also encourages them to strive for excellence. By celebrating the achievements and progress made towards the goals, software teams can be motivated to work harder and smarter to achieve even better results.
Moreover, acknowledging improvements in key DORA metrics creates a sense of ownership and responsibility among the team members, which in turn drives them to take initiative and work towards the common goal of achieving organizational success.
It is important to note that drawing conclusions solely based on the metrics provided by the Declaration on Research Assessment (DORA) can sometimes lead to inaccurate or misguided results.
To avoid such situations, it is essential to have a comprehensive understanding of the larger organizational context, including its goals, objectives, and challenges. This contextual understanding empowers stakeholders to use DORA metrics more effectively and make better-informed decisions.
Therefore, it is recommended that DORA metrics be viewed as part of a more extensive organizational framework to ensure that they are interpreted and utilized correctly.
Maintaining a balance between speed and stability is crucial for the long-term success of any system or process. While speed is a desirable factor, overemphasizing it can often result in a higher chance of errors and a greater change failure rate.
In such cases, when speed is prioritized over stability, the system may become prone to frequent crashes, downtime, and other issues that can ultimately harm the overall productivity and effectiveness of the system. Therefore, it is essential to ensure that speed and stability are balanced and optimized for the best possible outcome.
The DORA (DevOps Research and Assessment) metrics are widely used to measure the effectiveness and efficiency of software development teams covering aspects such as code quality and various workflow metrics. However, it is important to note that these metrics should not be used as a means to assign blame to individuals or teams.
Rather, they should be employed collaboratively to identify areas for improvement and to foster a culture of innovation and collaboration. By focusing on the collective goal of improving the software development process, teams can work together to enhance their performance and achieve better results.
It is crucial to approach DORA metrics as a tool for continuous improvement, rather than a means of evaluating individual performance. This approach can lead to more positive outcomes and a more productive work environment.
Continuous learning, which refers to the process of consistently acquiring new knowledge and skills, is fundamental for achieving success in both personal and professional life. In the context of DORA metrics, which stands for DevOps Research and Assessment, it is important to consider the learning aspect to ensure continuous improvement.
Neglecting this aspect can impede ongoing progress and hinder the ability to keep up with the ever-changing demands and requirements of the industry. Therefore, it is crucial to prioritize learning as an integral part of the DORA metrics to achieve sustained success and growth.
Benchmarking is a useful tool for organizations to assess their performance, identify areas for improvement, and compare themselves to industry standards. However, it is important to note that relying solely on benchmarking can be limiting.
Every organization has unique circumstances that may require deviations from industry benchmarks. Therefore, it is essential to focus on tailored improvements that fit the specific needs of the organization. By doing so, software development teams can not only improve organizational performance but also achieve a competitive advantage within the industry.
To make the most out of data collection, it is crucial to have a well-defined plan for utilizing the data to drive positive change. The data collected should be relevant, accurate, and timely. The next step is to establish a feedback loop for analysis and implementation.
This feedback loop involves a continuous cycle of collecting data, analyzing it, making decisions based on the insights gained, and then implementing any necessary changes. This ensures that the data collected is being used to drive meaningful improvements in the organization.
The feedback loop should be well-structured and transparent, with clear communication channels and established protocols for data management. By setting up a robust feedback loop, organizations can derive maximum value from DORA metrics and ensure that their data collection efforts are making a tangible impact on their business operations.
When it comes to evaluating software delivery performance and fostering a culture of continuous delivery, relying solely on quantitative data may not provide a complete picture. This is where qualitative feedback, particularly from engineering leaders, comes into play, as it enables us to gain a more comprehensive and nuanced understanding of how our software delivery process is functioning.
Combining both quantitative DORA metrics and qualitative feedback can ensure that continuous delivery efforts are aligned with the strategic goals of the organization. Hence, empowering engineering leaders to make informed, data-driven decisions that drive better outcomes.
Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics, providing an efficient solution for development teams to seek precision in their DevOps performance measurement.
To effectively use DORA metrics and enhance developer productivity, organizations must approach them balanced with emphasis on understanding, alignment, collaboration, and continuous improvement. By following this approach, software teams can gain valuable insights to drive positive change and achieve engineering excellence with a focus on continuous delivery.
A holistic view of all aspects of software development helps identify key areas for improvement. Alignment ensures that everyone is working towards the same goals. Collaboration fosters communication and knowledge-sharing amongst teams. Continuous improvement is critical to engineering excellence, allowing organizations to stay ahead of the competition and deliver high-quality products and services to customers.
Adopting DevOps methods is crucial for firms aiming to achieve agility, efficiency, and quality in software development, which is a constantly changing terrain. The DevOps movement is both a cultural shift and a technological one; it promotes automation, collaboration, and continuous improvement among all parties participating in the software delivery lifecycle, from developers to operations.
The goal of DevOps is to improve software product quality, speed up development, and decrease time-to-market. Companies utilize metrics like DevOps Research and Assessment (DORA) to determine how well DevOps strategies are working and how to improve them.
DevOps is more than just a collection of methods; it's a paradigm change that encourages teams to work together, from development to operations. To accomplish common goals, DevOps practices eliminate barriers, enhance communication, and coordinate efforts. It guarantees consistency and dependability in software delivery and aims to automate processes to standardize and speed them up.
Foundational Concepts in DevOps:
If you want to know how well your DevOps methods are doing, look no further than the DORA metrics.
DORA metrics, developed by the DORA team, are key performance indicators that measure the effectiveness and efficiency of software development and delivery processes. They provide a data-driven approach to evaluate the impact of operational practices on software delivery performance.
To help organizations find ways to improve and make smart decisions, these metrics provide quantitative insights into software delivery. Four key DORA metrics are Lead Time, Deployment Frequency, Change Failure Rate, and Mean Time to Recover. Let's read more about them in detail below:
The lead time is the sum of all the steps required to go from ideation to production deployment of a code update. All the steps involved are contained in this, including:
Lead time can be affected by a number of things, such as:
Optimizing lead time: Teams can actively work to reduce lead time by focusing on:
Deployment Frequency measures how often changes to the code are pushed to production. Greater deployment frequency is an indication of increased agility and the ability to respond quickly to market demands. How often, in a specific time period, code updates are pushed to the production environment. A team can respond to client input, enhance their product, and supply new features and repairs faster with a greater Deployment Frequency.
Approaches for maximizing the frequency of deployments:
The choice between quality and stability and high Deployment Frequency should be carefully considered. Achieving success in the long run requires striking a balance between speed and quality. Optimal deployment frequencies will vary between teams and organizations due to unique requirements and limitations.
Change Failure Rate measures what proportion of changes fail or need quick attention after deployment. It helps you evaluate how well your testing and development procedures are working.
How to calculate CFR - Total unsuccessful changes divided by total deployed changes. To get a percentage, multiply by 100.
CFR Tracking Benefits
Approaches for CFR reduction
MTTR evaluates the average production failure recovery time. Low MTTR means faster incident response and system resiliency. MTTR is an important system management metric, especially in production.
How to calculate MTTR : It is calculated by dividing the total time spent recovering from failures by the total number of failures over a specific period. After an incident, it estimates the average time to restore a system to normal.
Advantages from a low MTTR
Factors impact MTTR, including
Organizations can optimize MTTR with techniques like
The adoption of DORA metrics brings several advantages to organizations:
Value Stream Management refers to delivering frequent, high-quality releases to end-users. The success metric for value stream management is customer satisfaction i.e. realizing the value of the changes.
DORA DevOps metrics play a key role in value stream management as they offer baseline measures including:
By incorporating customer feedback, DORA metrics help DevOps teams identify potential bottlenecks and strategically position their services against competitors.
New features and updates must be deployed quickly in competitive e-commerce. E-commerce platforms can enhance deployment frequency and lead time with DORA analytics.
An e-commerce company implements DORA metrics but finds that manual testing takes too long to deploy frequently. They save lead time and boost deployment frequency by automating testing and streamlining CI/CD pipelines. This lets businesses quickly release new features and upgrades, giving them an edge.
In the financial industry, dependability and security are vital, thus failures and recovery time must be minimized. DORA measurements can reduce change failures and incident recovery times.
Financial institutions detect high change failure rates during transaction processing system changes. DORA metrics reveal failure causes including testing environment irregularities. Improvements in infrastructure as code and environment management reduce failure rates and mean time to recovery, making client services more reliable.
In healthcare, where software directly affects patient care, deployment optimization and failure reduction are crucial. DORA metrics reduce change failure and deployment time.
For instance, a healthcare software provider discovers that manual approval and validation slow rollout. They speed deployment by automating compliance checks and clarifying approval protocols. They also improve testing procedures to reduce change failure. This allows faster system changes without affecting quality or compliance, increasing patient care.
Tech businesses that want to grow quickly must provide products and upgrades quickly. DORA metrics improve deployment lead time.
A tech startup examines DORA metrics and finds that manual configuration chores slow deployments. They automate configuration management and provisioning with infrastructure as code. Thus, their deployment lead time diminishes, allowing businesses to iterate and innovate faster and attract more users and investors.
Even in manufacturing, where software automates and improves efficiency, deployment methods must be optimized. DORA metrics can speed up and simplify deployment.
A manufacturing company uses IoT devices to monitor production lines in real time. However, updating these devices is time-consuming and error-prone. DORA measurements help them improve version control and automate deployment. This optimises production by reducing deployment time and ensuring more dependable and synchronised IoT device updates.
Typo is a leading AI-driven engineering analytics platform that provides SDLC visibility, data-driven insights, and workflow automation for software development teams. It provides comprehensive insights through DORA and other key metrics in a centralized dashboard.
Adopting DevOps and leveraging DORA metrics is crucial for modern software development. DevOps metrics drive collaboration and automation, while DORA metrics offer valuable insights to streamline delivery processes and boost team performance. Together, they help teams deliver higher-quality software faster and stay ahead in a competitive market.
Are you familiar with the term Change Failure Rate (CFR)? It's one of the key DORA metrics in DevOps that measures the percentage of failed changes out of total implementations. This metric is pivotal for development teams in assessing the reliability of the deployment process.
CFR, or Change Failure Rate metric measures the frequency at which newly deployed changes lead to failures, glitches, or unexpected outcomes in the IT environment. It reflects the stability and reliability of the entire software development and deployment lifecycle. By tracking CFR, teams can identify bottlenecks, flaws, or vulnerabilities in their processes, tools, or infrastructure that can negatively impact the quality, speed, and cost of software delivery.
Lowering CFR is a crucial goal for any organization that wants to maintain a dependable and efficient deployment pipeline. A high CFR can have serious consequences, such as degraded service, delays, rework, customer dissatisfaction, revenue loss, or even security breaches. To reduce CFR, teams need to implement a comprehensive strategy involving continuous testing, monitoring, feedback loops, automation, collaboration, and culture change. By optimizing their workflows and enhancing their capabilities, teams can increase agility, resilience, and innovation while delivering high-quality software at scale.
Change failure rate measures software development reliability and efficiency. It’s related to team capacity, code complexity, and process efficiency, impacting speed and quality. Change Failure Rate calculation is done by following these steps:
Identify Failed Changes: Keep track of the number of changes that resulted in failures during a specific timeframe.
Determine Total Changes Implemented: Count the total changes or deployments made during the same period.
Apply the formula:
Use the formula CFR = (Number of Failed Changes / Total Number of Changes) * 100 to calculate the Change Failure Rate as a percentage.
Here is an example: Suppose during a month:
Failed Changes = 5
Total Changes = 100
Using the formula: (5/100)*100 = 5
Therefore, the Change Failure Rate for that period is 5%.
It only considers what happens after deployment and not anything before it. 0% - 15% CFR is considered to be a good indicator of your code quality.
Low change failures mean that the code review and deployment process needs attention. To reduce it, the team should focus on reducing deployment failures and time wasted due to delays, ensuring a smoother and more efficient software delivery performance.
With Typo, you can improve dev efficiency and team performance with an inbuilt DORA metrics dashboard.
Stability is pivotal in software deployment. The change Failure Rate measures the percentage of changes that fail. A high failure rate could signify inadequate testing, poor code quality, or insufficient quality control. Enhancing testing protocols, refining the code review process, and ensuring thorough documentation can reduce the failure rate, enhancing overall stability and team performance.
Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.
Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.
Change Failure Rate (CFR) is more than just a metric and is an essential indicator of an organization's software development health. It encapsulates the core aspects of resilience and efficiency within the software development life cycle.
The CFR (Change Failure Rate) reflects how well an organization's software development practices can handle changes. A low CFR indicates the organization can make changes with minimal disruptions and failures. This level of resilience is a testament to the strength of their processes, showing their ability to adapt to changing requirements without difficulty.
Efficiency lies at the core of CFR. A low CFR indicates that the organization has streamlined its deployment processes. It suggests that changes are rigorously tested, validated, and integrated into the production environment with minimal disruptions. This efficiency is not just a numerical value, but it reflects the organization's dedication to delivering dependable software.
A high change failure rate, on the other hand, indicates potential issues in the deployment pipeline. It serves as an early warning system, highlighting areas that might affect system reliability. Identifying and addressing these issues becomes critical in maintaining a reliable software infrastructure.
The essence of CFR (Change Failure Rate) lies in its direct correlation with the overall reliability of a system. A high CFR indicates that changes made to the system are more likely to result in failures, which could lead to service disruptions and user dissatisfaction. Therefore, it is crucial to understand that the essence of CFR is closely linked to the end-user experience and the trustworthiness of the deployed software.
The Change Failure Rate (CFR) is a crucial metric that evaluates how effective an organization's IT practices are. It's not just a number - it affects different aspects of organizational performance, including customer satisfaction, system availability, and overall business success. Therefore, it is important to monitor and improve it.
Efficient IT processes result in a low CFR, indicating a reliable software deployment pipeline with fewer failed deployments.
Organizations can identify IT weaknesses by monitoring CFR. High CFR patterns highlight areas that require attention, enabling proactive measures for software development.
CFR directly influences customer satisfaction. High CFR can cause service issues, impacting end-users. Low CFR results in smooth deployments, enhancing user experience.
The reliability of IT systems is critical for business operations. A lower CFR implies higher system availability, reducing the chances of downtime and ensuring that critical systems are consistently accessible.
Efficient IT processes are reflected in a low CFR, which contributes to operational efficiency. This, in turn, positively affects overall business success by streamlining development workflows and reducing the time to market for new features or products.
A lower CFR means fewer post-deployment issues and lower costs for resolving problems, resulting in potential revenue gains. This financial aspect is crucial to the overall success and sustainability of the organization.
Organizations can improve software development by proactively addressing issues highlighted by CFR.
Organizations can enhance IT resilience by identifying and mitigating factors contributing to high CFR.
CFR indirectly contributes to security by promoting stable and reliable deployment practices. A well-maintained CFR reflects a disciplined approach to changes, reducing the likelihood of introducing vulnerabilities into the system.
Implementing strategic practices can optimize the Change Failure Rate (CFR) by enhancing software development and deployment reliability and efficiency.
Implementing automated testing and deployment processes is crucial for minimizing human error and ensuring the consistency of deployments. Automated testing catches potential issues early in the development cycle, reducing the likelihood of failures in production.
Leverage CI/CD pipelines for automated integration and deployment of code changes, streamlining the delivery process for more frequent and reliable software updates.
Establishing a robust monitoring system that detects issues in real time during the deployment lifecycle is crucial. Continuous monitoring provides immediate feedback on the performance and stability of applications, enabling teams to promptly identify and address potential problems.
Implement mechanisms to proactively alert relevant teams of anomalies or failures in the deployment pipeline. Swift response to such notifications can help minimize the potential impact on end-users.
Foster collaboration between development and operations teams through DevOps practices. Encourage cross-functional communication and shared responsibilities to create a unified software development and deployment approach.
Efficient communication channels & tools facilitate seamless collaboration, ensuring alignment & addressing challenges.
Create feedback loops in development and deployment. Collect feedback from the team, and users, and monitor tools for improvement.
It's important to have regular retrospectives to reflect on past deployments, gather insights, and refine deployment processes based on feedback. Strive for continuous improvement.
Empower software development teams with tools, training, and a culture of continuous improvement. Encourage a blame-free environment that promotes learning from failures. CFR is one of the key metrics and critical performance metrics of DevOps maturity. Understanding its implications and implementing strategic optimizations is a great way to enhance deployment processes, ensuring system reliability and contributing to business success.
Typo provides an all-inclusive solution if you're looking for ways to enhance your team's productivity, streamline their work processes, and build high-quality software for end-users.
Understanding and optimizing key metrics is crucial in the dynamic landscape of software development. One such metric, Lead Time for Changes, is a pivotal factor in the DevOps world. Let's delve into what this metric entails and its significance in the context of DORA (DevOps Research and Assessment) metrics.
Lead Time for Changes is a critical metric used to measure the efficiency and speed of software delivery. It is the duration between a code change being committed and its successful deployment to end-users.
The measurement of this metric offers valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies. By analyzing the Change lead time, development teams can identify bottlenecks in the delivery pipeline and streamline their workflows to improve software delivery's overall speed and efficiency. Therefore, it is crucial to track and optimize this metric.
This metric is a good indicator of the team’s capacity, code complexity, and efficiency of the software development process. It is correlated with both the speed and quality of the engineering team, which further impacts cycle time.
Lead time for changes measures the time that passes from the first commit to the eventual deployment of code.
To measure this metric, DevOps should have:
Divide the total sum of time spent from commitment to deployment by the number of commitments made. Suppose, the total amount of time spent on a project is 48 hours. The total number of commits made during that time is 20. This means that the lead time for changes would be 2.4 hours. In other words, an average of 2.4 hours are required for a team to make changes and progress until deployment time.
A shorter lead time means more efficient a DevOps team is in deploying code, differentiating elite performers from low performers.
Longer lead times can signify the testing process is obstructing the CI/CD pipeline. It can also limit the business’s ability to deliver value to the end users. Hence, install more automated deployment and review processes. It further divides production and features into much more manageable units.
With Typo, you can improve dev efficiency with an inbuilt DORA metrics dashboard.
Picture your software development team tasked with a critical security patch. Measuring change lead time helps pinpoint the duration from code commit to deployment. If it goes for a long run, bottlenecks in your CI/CD pipelines or testing processes might surface. Streamlining these areas ensures rapid responses to urgent tasks.
Teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.
A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.
The size of a pull request (PR) profoundly influences overall lead time. Large PRs require more review time hence delaying the process of code review adding to the overall lead time (longer lead times). Dividing large tasks into manageable portions accelerates deployments, and reduces deployment time addressing potential bottlenecks effectively.
At its core, a mean lead time for Changes of the entire development process reflects its agility. It encapsulates the entire journey of a code change, from conception to production, offering insights into workflow efficiency and identifying potential bottlenecks.
Agility is a crucial aspect of software development that enables organizations to keep up with the ever-evolving landscape. It is the ability to respond swiftly and effectively to changes while maintaining a balance between speed and stability in the development life cycle. Agility can be achieved by implementing flexible processes, continuous integration and continuous delivery, automated testing, and other modern development practices that enable software development teams to pivot and adapt to changing business requirements quickly.
Organizations that prioritize agility are better equipped to handle unexpected challenges, stay ahead of competitors, and deliver high-quality software products that meet the needs of their customers.
The development pipeline has several stages: code initiation, development, testing, quality assurance, and final deployment. Each stage is critical for project success and requires attention to detail and coordination. Code initiation involves planning and defining the project.
Development involves coding, testing, and collaboration. Testing evaluates the software, while quality assurance ensures it's bug-free. Final deployment releases the software. This pipeline provides a comprehensive view of the process for thorough analysis.
Measuring the duration of each stage of development is a critical aspect of workflow analysis. Quantifying the time taken by each stage makes it possible to identify areas where improvements can be made to streamline processes and reduce unnecessary delays.
This approach offers a quantitative measure of the efficiency of each workflow, highlighting areas that require attention and improvement. By tracking the time taken at each stage, it is possible to identify bottlenecks and other inefficiencies that may be affecting the overall performance of the workflow. This information can then be used to develop strategies for improving workflow efficiency, reducing costs, and improving the final product or service quality.
It can diagnose and identify specific stages or processes causing system delays. It helps devops teams to proactively address bottlenecks by providing detailed insights into the root causes of delays. By identifying these bottlenecks, teams can take corrective action to enhance overall efficiency and reduce lead time.
It is particularly useful in complex systems where delays may occur at multiple stages, and pinpointing the exact cause of a delay can be challenging. With this tool, teams can quickly and accurately identify the source of the bottleneck and take corrective action to improve the system's overall performance.
The importance of Lead Time for Changes cannot be overstated. It directly correlates with an organization's performance, influencing deployment frequency and the overall software delivery performance. A shorter lead time enhances adaptability, customer satisfaction, and competitive edge.
Short lead times have a significant impact on an organization's performance. They allow organizations to respond quickly to changing market conditions and customer demands, improving time-to-market, customer satisfaction, and operational efficiency.
Low lead times in software development allow high deployment frequency, enabling rapid response to market demands and improving the organization's ability to release updates, features, and bug fixes. This helps companies stay ahead of competitors, adapt to changing market conditions, and reduce the risks associated with longer development cycles.
High velocity is essential for the software delivery performance. By streamlining the process, improving collaboration, and removing bottlenecks, new features and improvements can be delivered quickly, resulting in better user experience and increased customer satisfaction. A high delivery velocity is essential for remaining competitive.
Shorter lead times have a significant impact on organizational adaptability and customer satisfaction. When lead times are reduced, businesses can respond more quickly to changes in the market, customer demands, and internal operations. This increased agility allows companies to make adjustments faster and with less risk, improving customer satisfaction.
Additionally, shorter lead times can lower inventory costs and improve cash flow, as businesses can more accurately forecast demand and adjust their production and supply chain accordingly. Overall, shorter lead times are a key factor in building a more efficient and adaptable organization.
To stay competitive, businesses must minimize lead time. This means streamlining software development, optimizing workflows, and leveraging automation tools to deliver products faster, cut costs, increase customer satisfaction, and improve the bottom line.
Organizations can employ various strategies to optimize Lead Time for Changes. These may include streamlining development workflows, adopting automation, and fostering a culture of continuous improvement.
The process of development optimization involves analyzing each stage of the development process to identify and eliminate any unnecessary steps and delays. The ultimate goal is to streamline the process and reduce the time it takes to complete a project. This approach emphasizes the importance of having a well-defined and efficient workflow, which can improve productivity, increase efficiency, and reduce the risk of errors or mistakes. By taking a strategic and proactive approach to development optimization, businesses can improve their bottom line by delivering projects more quickly and effectively while also improving customer satisfaction and overall quality.
Automation tools play a crucial role in streamlining workflows, especially when it comes to handling repetitive and time-consuming tasks. With the help of automation tools, businesses can significantly reduce manual intervention, minimize the likelihood of errors, and speed up their development cycle.
By automating routine tasks such as data entry, report generation, and quality assurance, employees can focus on more strategic and high-value activities, leading to increased productivity and efficiency. Moreover, automation tools can be customized to fit the specific needs of a business or a project, providing a tailored solution to optimize workflows.
Regular assessment and enhancement of development processes are crucial for maintaining high-performance levels. This promotes continual learning and adaptation to industry best practices, ensuring software development teams stay up-to-date with the latest technologies and methodologies. By embracing a culture of continuous improvement, organizations can enhance efficiency, productivity, and competitive edge.
Regular assessments and faster feedback allow teams to identify and address inefficiencies, reduce lead time for changes, and improve software quality. This approach enables organizations to stay ahead by adapting to changing market conditions, customer demands, and technological advancements.
Lead Time for Changes is a critical metric within the DORA framework. Its efficient management directly impacts an organization's competitiveness and ability to meet market demands. Embracing optimization strategies ensures a speedier software delivery process and a more resilient and responsive development ecosystem.
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In today's fast-paced software development industry, measuring and enhancing the efficiency of development processes is becoming increasingly important. The DORA Metrics framework has gained significant attention, and one of its essential components is Development Frequency. This blog post aims to comprehensively understand this metric by delving into its significance, impact on the organization's performance, and deployment optimization strategies.
In the world of DevOps, the Deployment Frequency metric reigns supreme. It measures the frequency of code deployment to production and reflects an organization's efficiency, reliability, and software delivery quality. By achieving an optimal balance between speed and stability, organizations can achieve agility, efficiency, and a competitive edge.But Development Frequency is more than just a metric; it's a catalyst for continuous delivery and iterative development practices that align seamlessly with the principles of DevOps. It helps organizations maintain a balance between speed and stability, which is a recurring challenge in software development.When organizations achieve a high Development Frequency, they can enjoy rapid releases without compromising the software's robustness. This can be a powerful driver of agility and efficiency, making it an essential component of software development.
Deployment frequency is often used to track the rate of change in software development and to highlight potential areas for improvement. It is important to measure Deployment Frequency for the following reasons:
Deployment Frequency is measured by dividing the number of deployments made during a given period by the total number of weeks/days. For example: If a team deployed 6 times in the first week, 7 in the second week, 4 in the third week, and 7 in the fourth week. Then, the deployment frequency is 6 per week.
One deployment per week is standard. However, it also depends on the type of product.
Teams that fall under the low performers category can install more automated processes. Such as for testing and validating new code and minimizing the time span between error recovery time and delivery.
Note that this is the first key metric. If the team takes the wrong approach in the first step, it can lead to the degradation of other DORA metrics as well.
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There are various ways to calculate Deployment Frequency. These include :
One of the easiest ways to calculate Deployment Frequency is by counting the number of deployments in a given time period. It can be done either by manually counting the number of deployments or by using a tool to calculate deployments such as a version control system or deployment pipeline.
Deployment Frequency can also be calculated by measuring the time it takes for code changes to be deployed in production. It can be done in two ways:
The deployment rate can be measured by the number of deployments per unit of time including deployments per day or per week. This can be dependent on the rhythm of your development and release cycles.
Another way of measuring Deployment Frequency is by counting the number of A/B tests launched during a given time period.
Achieving a balance between fast software releases and maintaining a stable software environment is a subtle skill. It requires a thorough understanding of trade-offs and informed decision-making to optimize both. Development Frequency enables organizations to achieve faster release cycles, allowing them to respond promptly to market demands, while ensuring the reliability and integrity of their software.
Frequent software development plays a crucial role in reducing lead time and allows organizations to respond quickly to market dynamics and customer feedback. The ability to frequently deploy software enhances an organization's adaptability to market demands and ensures swift responses to valuable customer feedback.
Development Frequency cultivates a culture of constant improvement by following iterative software development practices. Accepting change as a standard practice rather than an exception is encouraged. Frequent releases enable quicker feedback loops, promoting a culture of learning and adaptation. Detecting and addressing issues at an early stage and implementing effective iterations become an integral part of the development process.
Frequent software development is directly linked to improved business agility. This means that organizations that develop and deploy software more often are better equipped to respond quickly to changes in the market and stay ahead of the competition.
With frequent deployments, organizations can adapt and meet the needs of their customers with ease, while also taking advantage of new opportunities as they arise. This adaptability is crucial in today's fast-paced business environment, and it can help companies stay competitive and successful.
High Development Frequency does not compromise software quality. Instead, it often leads to improved quality by dispelling misconceptions associated with infrequent deployments. Emphasizing the role of Continuous Integration, Continuous Deployment (CI/CD), automated testing, and regular releases elevates software quality standards.
Having a robust automation process, especially through Continuous Integration/Continuous Delivery (CI/CD) pipelines, is a critical factor in optimizing Development Frequency. This process helps streamline workflows, minimize manual errors, and accelerate release cycles. CI/CD pipelines are the backbone of software development as they automate workflows and enhance the overall efficiency and reliability of the software delivery pipeline.
Microservices architecture promotes modularity by design. This architectural choice facilitates independent deployment of services and aligns seamlessly with the principles of high development frequency. The modular nature of microservices architecture enables individual component releases, ensuring alignment with the goal of achieving high development frequency.
Efficient feedback loops are essential for the success of Development Frequency. They enable rapid identification of issues, enabling timely resolutions. Comprehensive monitoring practices are critical for identifying and resolving issues. They significantly contribute to maintaining a stable and reliable development environment.
Development Frequency is not just any metric; it's the key to unlocking efficient and agile DevOps practices. By optimizing your development frequency, you can create a culture of continuous learning and adaptation that will propel your organization forward. With each deployment, iteration, and lesson learned, you'll be one step closer to a future where DevOps is a seamless, efficient, and continuously evolving practice. Embrace the frequency, tackle the challenges head-on, and chart a course toward a brighter future for your organization.
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Key Performance Indicators (KPIs) are the informing factors and draw paths for teams in the dynamic world of software development, where growth depends on informed decisions and concentrated efforts. In this in-depth post, we explore the fundamental relevance of software development KPIs and how to recognize, pick, and effectively use them.
Key performance indicators are the compass that software development teams use to direct their efforts with purpose, enhance team productivity, measure their progress, identify areas for improvement, and ultimately plot their route to successful outcomes. Software development metrics while KPIs add context and depth by highlighting the measures that align with business goals.
Using key performance indicators is beneficial for both team members and organizations. Below are some of the benefits of KPIs:
Key performance indicator such as cycle time helps in optimizing continuous delivery processes. It further assists in streamlining development, testing, and deployment workflows. Hence, resulting in quicker and more reliable feature releases.
KPIs also highlight resource utilization patterns. Engineering leaders can identify if team members are overutilized or underutilized. This helps in allowing for better resource allocation to avoid burnout and to balance the workloads.
KPIs assist in prioritizing new features effectively. Through these, software engineers and developers can identify which features contribute the most to key objectives.
In software development, KPIs and software metrics serve as vital tools for software developers and engineering leaders to keep track of their processes and outcomes.
It is crucial to distinguish software metrics from KPIs. While KPIs are the refined insights drawn from the data and polished to coincide with the broader objectives of a business, metrics are the raw, unprocessed information. Tracking the number of lines of code (LOC) produced, for example, is only a metric; raising it to the status of a KPI for software development teams falls short of understanding the underlying nature of progress.
Selecting the right KPIs requires careful consideration. It's not just about analyzing data, but also about focusing your team's efforts and aligning with your company's objectives.
Choosing KPIs must be strategic, intentional, and shaped by software development fundamentals. Here is a helpful road map to help you find your way:
Collaboration is at the core of software development. KPIs should highlight team efficiency as a whole rather than individual output. The symphony, not the solo, makes a work of art.
Let quality come first. The dimensions of excellence should be explored in KPIs. Consider measurements that reflect customer happiness or assess the efficacy of non-production testing rather than just adding up numbers.
Introspectively determine your key development processes before choosing KPIs. Let the KPIs reflect these crucial procedures, making them valuable indications rather than meaningless measurements.
Mindlessly copying KPIs may be dangerous, even if learning from others is instructive. Create KPIs specific to your team's culture, goals, and desired trajectory.
Team agreement is necessary for the implementation of KPIs. The KPIs should reflect the team's priorities and goals and allow the team to own its course. It also helps in increasing team morale and productivity.
To make a significant effect, start small. Instead of overloading your staff with a comprehensive set of KPIs, start with a narrow cluster and progressively add more as you gain more knowledge.
These nine software development KPIs go beyond simple measurements and provide helpful information to advance your development efforts.
The induction period for new members is crucial in the fire of collaboration. Calculate how long it takes a beginner to develop into a valuable contributor. A shorter induction period and an effective learning curve indicate a faster production infusion. Swift integration increases team satisfaction and general effectiveness, highlighting the need for a well-rounded onboarding procedure.
Effective onboarding may increase employee retention by 82%, per a Glassdoor survey. A new team member is more likely to feel appreciated and engaged when integrated swiftly and smoothly, increasing productivity.
Strong quality assurance is necessary for effective software. Hence, testing efficiency is a crucial KPI. Merge metrics for testing branch coverage, non-production bugs, and production bugs. The objective is to develop robust testing procedures that eliminate manufacturing flaws, Improve software quality, optimize procedures, spot bottlenecks, and avoid problems after deployment by evaluating the effectiveness of pre-launch evaluations.
A Consortium for IT Software Quality (CISQ) survey estimates that software flaws cost the American economy $2.84 trillion yearly. Effective testing immediately influences software quality by assisting in defect mitigation and lowering the cost impact of software failures.
The core of efficient development is beyond simple code production; it is an art that takes the form of little rework, impactful code modifications, and minimal code churn. Calculate the effectiveness of code modifications and strive to produce work beyond output and representing impact. This KPI celebrates superior coding and highlights the inherent worth of pragmatistically considerate coding.
In 2020, the US incurred a staggering cost of approximately $607 billion due to software bugs, as reported by Herb Kranser in "The Cost of Poor Software Quality in the US. Effective development immediately contributes to cost reduction and increased software quality, as seen by less rework, effective coding, and reduced code churn.
The user experience is at the center of software development. It is crucial for quality software products, engineering teams, and project managers. With surgical accuracy, assess user happiness. Metrics include feedback surveys, use statistics, and the venerable Net Promoter Score (NPS). These measurements combine to reveal your product's resonance with its target market. By decoding user happiness, you can infuse your development process with meaning and ensure alignment with user demands and corporate goals. These KPIs can also help in improving customer retention rates.
According to a PwC research, 73% of consumers said that the customer experience heavily influences their buying decisions. The success of your software on the market is significantly impacted by how well you can evaluate user happiness using KPIs like NPS.
Cycle time is the main character in the complex ballet that is development. Describe the process from conception to deployment in production. The tangled paths of planning, designing, coding, testing, and delivery are traversed by this KPI. Spotting bottlenecks facilitates process improvement, and encouraging agility allows accelerated results.Cycle time reflects efficiency and is essential for achieving lean and effective operations. In line with agile principles, cycle time optimization enables teams to adapt more quickly to market demands and provide value more often.
Although no program is impervious to flaws, stability and observability are crucial. Watch the Mean Time To Detect (MTTD), Mean Time To Recover (MTTR), and Change Failure Rate (CFR). This trio (the key areas of DORA metrics) faces the consequences of manufacturing flaws head-on. Maintain stability and speed up recovery by improving defect identification and action. This KPI protects against disruptive errors while fostering operational excellence.
Increased deployment frequency and reduced failure rates are closely correlated with focusing on production stability and observability in agile software development.
A team's happiness and well-being are the cornerstones of long-term success. Finding a balance between meeting times and effective work time prevents fatigue. A happy, motivated staff enables innovation. Prioritizing team well-being and happiness in the post-pandemic environment is not simply a strategy; it is essential for excellence in sustainable development.
Happy employees are also 20% more productive! Therefore, monitoring team well-being and satisfaction using KPIs like the meeting-to-work time ratio ensures your workplace is friendly and productive.
The software leaves a lasting impact that transcends humans. Thorough documentation prevents knowledge silos. To make transitions easier, measure the coverage of the code and design documentation. Each piece of code that is thoroughly documented is an investment in continuity. Protecting collective wisdom supports unbroken development in the face of team volatility as the industry thrives on evolution.
Teams who prioritize documentation and knowledge sharing have 71% quicker issue resolution times, according to an Atlassian survey. Knowledge transfer is facilitated, team changes are minimized, and overall development productivity is increased through effective documentation KPIs.
Software that works well is the result of careful preparation. Analyze the division of work, predictability, and WIP count—prudent task segmentation results in a well-structured project. Predictability measures commitment fulfillment and provides information for ongoing development. To speed up the development process and foster an efficient, focused development journey, strive for optimum WIP management.
According to Project Management Institute (PMI) research, 89% of projects are completed under budget and on schedule by high-performing firms. Predictability and WIP count are task planning KPIs that provide unambiguous execution routes, effective resource allocation, and on-time completion, all contributing to project success.
Implementing these key performance indicators is important for aligning developers' efforts with strategic objectives and improving the software delivery process.
Understand the strategic goals of your organization or project. It can include purposes related to product quality, time to market, customer satisfaction, or revenue growth.
Choose KPIs that are directly aligned with your strategic goals. Such as for code quality: code coverage or defect density can be the right KPI. For team health and adaptability, consider metrics like sprint burndown or change failure rate.
Track progress by continuously monitoring software engineering KPIs such as sprint burndown and team velocity. Regularly analyze the data to identify trends, patterns, and blind spots.
Share KPIs results and progress with your development team. Transparency results in accountability. Hence, ensuring everyone is aligned with the business objectives as well as aware of the goal setting.
These 9 KPIs are essential for software development. They give insight into every aspect of the process and help teams grow strategically, amplify quality, and innovate for the user. Remember that each indicator has significance beyond just numbers. With these KPIs, you can guide your team towards progress and overcome obstacles. You have the compass of software expertise at your disposal.
By successfully incorporating these KPIs into your software development process, you may build a strong foundation for improving code quality, increasing efficiency, and coordinating your team's efforts with overall business objectives. These strategic indicators remain constant while the software landscape changes, exposing your route to long-term success.
Agile has transformed the way companies work. It reduces the time to deliver value to end-users and lowers the cost. In other words, Agile methodology helps ramp up the developers teams’ efficiency.
But to get the full benefits of agile methodology, teams need to rely on agile metrics. They are realistic and get you a data-based overview of progress. They help in measuring the success of the team.
Let’s dive deeper into Agile metrics and a few of the best-known metrics for your team:
Agile metrics can also be called Agile KPIs. These are the metrics that you use to measure the work of your team across SDLC phases. It helps identify the process's strengths and expose issues, if any, in the early stages.Besides this, Agile metrics help cover different aspects including productivity, quality, and team health.
A few benefits of Agile metrics are:
With the help of agile project metrics, development teams can identify areas for improvement, track progress, and make informed decisions. This enhances efficiency which further increases team productivity.
Agile performance metrics provide quantifiable data on various aspects of work. This creates a shared understanding among team members, stakeholders, and leadership. Hence, contributing to a more accountable and transparent development environment.
These meaningful metrics provide valuable insights into various aspects of the team's performance, processes, and outcomes. This makes it easy to assess progress and address blind spots. Therefore, fostering a culture that values learning, adaption, and ongoing improvement.
Agile metrics including burndown chart, escaped defect rate, and cycle time provide software development teams with data necessary to optimize the development process and streamline workflow. This enables teams to prioritize effectively. Hence, ensuring delivered features meet user needs and improve customer satisfaction.
This metric focuses on workflow, organizing and prioritizing work, and the amount of time invested to obtain results. It uses visual cues for tracking progress over time.
Scrum metrics focus on the predictable delivery of working software to customers. It analyzes sprint effectiveness and highlights the amount of work completed during a given sprint.
This metric focuses on productivity and quality of work output, flow efficiency, and eliminating wasteful activities. It helps in identifying blind spots and tracking progress toward lean goals.
Below are a few powerful agile metrics you should know about:
Lead time metric measures the total time elapsed from the initial request being made till the final product is delivered. In other words, it measures the entire agile system from start to end. The lower the lead time, the more efficient the entire development pipeline is.
Lead time helps keep the backlog lean and clean. This metric removes any guesswork and predicts when it will start generating value. Besides this, it helps in developing a business requirement and fixing bugs.
This popular metric measures how long it takes to complete tasks. Less cycle time ensures more tasks are completed. When the cycle time exceeds a sprint, it signifies that the team is not completing work as it is supposed to. This metric is a subset of lead time.
Moreover, cycle time focuses on individual tasks. Hence, a good indicator of the team’s performance and raises red flags, if any in the early stages.
Cycle time makes project management much easier and helps in detecting issues when they arise.
This agile metric indicates the average amount of work completed in a given time, typically a sprint. It can be measured with hours or story points. As it is a result metric, it helps measure the value delivered to customers in a series of sprints. Velocity predicts future milestones and helps in estimating a realistic rate of progress.
The higher the team’s velocity, the more efficient teams are at developing processes.
Although, the downside of this metric is that it can be easily manipulated by teams when they have to satisfy velocity goals.
The sprint burndown chart helps in knowing how many story points have been completed and are remaining during the sprint. The output is measured in terms of hours, story points, or backlogs which allows you to assess your performance against the set parameters. As Sprint is time-bound, it is important to measure it frequently.
The most common ones include time (X-axis) and task (Y-axis).Sprint Burndown aims to get all forecasted work completed by the end of the sprint.
This metric demonstrates how many work items you currently have ‘in progress’ in your working process. It is an important metric that helps keep the team focused and ensures a continuous work flow. Unfinished work can result in sunk costs.
An increase in work in progress implies that the team is overcommitted and not using their time efficiently. Whereas, the decrease in work in progress states that the work is flowing through the system quickly and the team can complete tasks with few blockers.
Moreover, limited work in progress also has a positive effect on cycle time.
This is another agile metric that measures the number of tasks delivered per sprint. It can also be known as measuring story points per iteration. It represents the team’s productivity level. Throughput can be measured quarterly, monthly, weekly, per release, per iteration, and in many other ways.
It allows you in checking the consistency level of the team and identify how much software can be completed within a given period. Besides this, it can also help in understanding the effect of workflow on business performance.
But, the drawback of this metric is that it doesn’t show the starting points of tasks.
This agile metric tracks the coding process and measures how much of the source code is tested. It helps in giving a good perspective on the quality of the product and reflects the raw percentage of code coverage. It is measured by a number of methods, statements, conditions, and branches that comprise your unit testing suite.
When the code coverage is lower, it implies that the code hasn’t been thoroughly tested. It can further result in low quality and a high risk of errors. But, the downside of this metric is that it excludes other types of testing. Hence, higher code statistics may not always imply excellent quality.
This key metric reveals the quality of the products delivered and identifies the number of bugs discovered after the release enters production. Escape defects include changes, edits, and unfixed bugs.
It is a critical metric as it helps in identifying the loopholes and technical debt in the process. Hence, improving the production process.
Ideally, escape defects should be minimized to zero. As if the bugs are detected after release, it can result in cause immense damage to the product.
Cumulative flow diagram visualizes the team’s entire workflow. Color coding helps in showing the status of the tasks and quickly identify the obstacles in agile processes. For example, grey color represents the agile project scope, green shows completed tasks and other colored items represent the particular status of the tasks.
X-axis represents the time frame while Y-axis includes several tasks within the project.
This key metric help find bottlenecks and address them by making adjustments and improving the workflow.
One of the most overlooked metrics is the Happiness metric. It indicates how the team feels about their work. The happiness metric evaluates the team’s satisfaction and morale through a ranking on a scale. It is usually done through direct interviews or team surveys.The outcome helps in knowing whether the current work environment, team culture, and tools are satisfactory. It also lets you identify areas of improvement in practices and processes.
When the happiness metric is low yet other metrics show a positive result, it probably means that the team is burned out. It can negatively impact their morale and productivity in the long run.
We have mentioned the optimal well-known agile metrics. But, it is up to you which metrics you choose that can be relevant for your team and the requirements of end-users.
You can start with a single metric and add a few more. These metrics will not only help you see results tangibly but also let you take note of your team’s productivity.
The ticking clock of coding time is often considered a factor in making or breaking the success of a development project. When developers manage it well, teams can meet deadlines, deliver high-quality software, and foster collaboration.
However, sometimes coding times are high. This can cause many internal issues and affect the product development cycle.
This blog will address why coding time is high sometimes and how you can improve it.
Coding time is the time it takes from the first commit to a branch to the eventual submission of a pull request. It is a crucial part of the development process where developers write and refine their code based on the project requirements.
High coding time can lead to prolonged development cycles, affecting delivery timelines. Coding time is crucial in the software development lifecycle as it can directly impact the cycle time.
Thus, managing the coding time efficiently to ensure the code completion is done on time with quicker feedback loops and a frictionless development process is essential.
Maintaining the right coding time has several benefits for engineering teams.
When you reduce the coding time, developers can complete more tasks. This moves the project faster and results in shorter development cycles.
With less time spent on coding, developers can have enough time for collaborative activities such as code reviews. These are crucial for a team to function well and enable knowledge sharing.
When coding time is lesser, developers can focus more on quality by conducting testing and debugging processes. This results in cleaner code.
While less coding time has several benefits, this often isn’t the reality. However, high coding time is not just the result of a team member being lazy – several reasons cause high coding time.
Whenever the tasks or features are complicated, additional coding time is needed compared to the more straightforward tasks.
Developers also try to complete the entire tasks in one go which can be hard to achieve. This leads to the developer getting overwhelmed and, eventually, prolonging the coding time. Code review plays a vital role in this context, allowing for constructive feedback and ensuring the quality of the codebase.For software developers, breaking down work into smaller, more manageable chunks is crucial to make progress and stay focused. It’s important to commit small changes frequently to move forward quickly and receive feedback more often. This ensures that the development process runs smoothly and stays on track.
When the requirement is defined poorly, developers will struggle to be efficient. It leads to higher coding time in understanding the requirement, seeking clarification, and making assumptions based on this.
It is essential to establish clear and comprehensive requirements before starting any coding work. This helps developers create an accurate roadmap, pave the way for smoother debugging processes, and reduce the chances of encountering unexpected obstacles. Effective planning and scoping improve the efficiency of the coding process, resulting in timely and satisfactory outcomes.
In a team, there will be developers with different skillset and experience. Additionally, the expertise and familiarity of the developers with the codebase and the technology stack can affect their coding speed.
Maintaining focus and staying on-task while coding is crucial for efficient development. Task languishing is a common issue that can arise due to distractions or shifting priorities, leading to abandoned tasks and decreased productivity.
A survey showed that developers spent only one-third of their time writing new code but spent 35% managing code with code maintenance, testing, and solving security issues.
To avoid this, it’s essential to conduct regular progress reviews. Teams must implement a systematic review process to identify potential issues and address them promptly by reallocating resources as needed. Consistency and focus throughout the development cycle are key for optimizing coding time.
When a developer has too many ongoing projects, they are forced to frequently multitask and switch contexts. This can lead to a reduction in the amount of time they spend working on a particular branch or issue, resulting in an increase in their coding time metric.Use the worklog to understand the dev’s commits over a timeline to different issues. If a developer makes sporadic contributions to various issues, it may be indicative of frequent context switching during a sprint. To mitigate this issue, it is advisable to balance and rebalance the assignment of issues evenly and encourage the team to avoid multitasking by focusing on one task at a time. This approach can help reduce coding time.
Set goals for the work at risk where the rule of thumb is keeping the PR with less than 100 code changes and refactor size as above 50%.To achieve the team goal of reducing coding time, real-time Slack alerts can be utilised to notify the team of work at risk when large and heavily revised PRs are published. By using these alerts, it is possible to identify and address issues, story-points, or branches that are too extensive in scope and require breaking down.
Ensuring fast and efficient code reviews is crucial to optimize coding time. It’s important to inform developers of how timely reviews can speed up the entire development process.
To accomplish this, code review automation tools should be used to improve the review process. These tools can separate small reviews from large ones and automatically assign them to available developers. Furthermore, scheduling specialist reviews can guarantee that complex tasks receive the necessary attention without causing any undue delays.
Improving coding productivity necessitates the adoption of data-driven practices. Teams should incorporate code quality tools that can efficiently monitor coding time and project advancement.
Such tools facilitate the swift identification of areas that require attention, enabling developers to refine their methods continuously. Using data-driven insights is the key to developing more effective coding practices.
Before starting the coding process, thoroughly defining and clarifying the project requirements is extremely important. This crucial step guarantees that developers have a complete comprehension of what needs to be achieved, ultimately resulting in a successful outcome.
Pair programming involves two developers working together on the same code at the same time. This can help reduce coding time by allowing developers to collaborate and share ideas, which can lead to faster problem-solving and more efficient coding. Incorporating the code review process into the pair programming process also ensures the quality of the codebase.
Encouraging open communication and collaboration among team members is crucial to creating a productive and positive work environment. This fosters a culture of teamwork and enables efficient problem-solving through shared ideas. Working together leads to more significant achievements than individuals can accomplish alone.4. Automate Repetitive Processes: Utilize automation tools to streamline repetitive coding tasks, such as code generation or testing, to save time and effort.
Developers must always stay up to date with the latest technologies and best practices. This is crucial for increasing coding speed and efficiency while enhancing the quality of the code. Continuous learning and skill development are essential to maintain a competitive edge in the industry.
To manage workloads and assignments effectively, it is recommended to develop a habit of regularly reviewing the Insights tab, and identifying long PRs on a weekly or even daily basis. Additionally, examining each team member’s workload can provide valuable insights. By using this data collaboratively with the team, it becomes possible to allocate resources more effectively and manage workloads more efficiently.
Using a framework, such as React or Angular, can help reduce coding time by providing pre-built components and libraries that can be easily integrated into the application.
Rapid prototyping involves creating a quick and simple version of the application to test its functionality and usability. This can help reduce coding time by allowing developers to quickly identify and address any issues with the application.
Agile methodologies, such as Scrum and Kanban, emphasize continuous delivery and feedback, which can help reduce coding time by allowing developers to focus on delivering small, incremental improvements to the application.
Reusing code that has already been written can help reduce coding time by eliminating the need to write code from scratch. This can be achieved by using code libraries, modules, and templates.
Incorporating artificial intelligence tools can enhance productivity by automating code review and repetitive tasks, minimizing coding errors, and accelerating the overall development cycle. These AI tools use various techniques including neural networks and machine learning algorithms to generate new content.
Typo provides instantaneous cycle time measurement for both the organization and each development team using their Git provider.
Our methodology divides cycle time into four phases:
When the coding time is high, your main dashboard will display the coding time as red.
Identify delay in the ‘Insights’ section at the team level and sort the teams by the cycle time. Further, click on the team to deep dive into cycle time breakdown of each team and see the delays in the coding time.
Coding times are the cornerstones of efficient software development. Thus, when its impact on project timelines is recognized, engineering teams can imbibe best practices and preventative strategies to deliver quality code on time.
PR cycle time and velocity are two commonly used metrics for measuring the efficiency and effectiveness of software development teams. These metrics help estimate how long your teams can complete a piece of work.
But, among these two, PR cycle time is often prioritized and preferred over velocity.
Therefore, in this blog, we understand the difference between these two metrics. Further, we will dive into the reason behind the PR cycle time over the velocity metric.
PR cycle time measures the process efficiency. In other words, it is the measurement of how much time it takes for your team to complete individual tasks from start to finish. It let them identify bottlenecks in the software development process and implement changes accordingly. Hence, allowing development work to flow smoother and faster through the delivery process.
PR Cycle time lets team members understand how efficiently they are working. A shorter PR cycle time means developers are spending less time waiting for code reviews and integration of code. Hence, indicates a high level of efficiency.
A shorter PR Cycle time means that features or updates can be released to end-users sooner. As a result, it helps them to stay competitive and meet customer demands.
Short PR Cycle time is a key component of agile software development. Hence, allows team members to adapt to changing requirements more easily.
Velocity is the measurement of team efficiency. It estimates how many story points an agile team can complete within a sprint. This metric is usually measured in weeks. As a result, it helps developer teams to plan and decide how much work to include in future sprints. But, the downside is that it doesn’t consider the quality of work or the time it takes to complete individual tasks.
By understanding development velocity, engineering managers and stakeholders can allocate resources more effectively. Hence, it ensures that development teams are neither overburdened or underutilized.
When velocity improves, it gives team members a sense of satisfaction from constantly delivering high-quality products. Hence, it improves their morale and allows them to collaborate with each other effectively.
A decline in velocity metrics signifies potential issues within the development process which includes team conflicts or technical debt. Hence, it allows us to address the issues early to maintain productivity.
PR cycle time is a more objective unit of measurement compared to story points in the production process. While many organizations use story points to estimate time-bound work since it is subjective. Hence, it is easy to manipulate. To increase velocity, you have to overestimate how long it will take to complete the task and therefore, add a larger number to your issue tracker.
Although PR cycle time may also be manipulated, it is most likely to work in your favor. By this, lowering the cycle time allows you to complete the work measurably faster. This further allows you to identify and fix blind spots quickly.
As a result, PR cycle time is a more challenging and tangible goal.
PR cycle time, an essential component of continuous improvement, improves your team’s ability to plan and estimate work. It gives you an accurate picture of how long it will take to move through the development process. Hence, offering real-time visibility and insights into a developer’s task. This further allows you to predict and forecast future work. In case, if the issue goes on longer than expected, you can discuss it with your team on a prior basis.
In the case of velocity, it cannot help in figuring out the why behind the work that took longer than expected. Hence, further planning and predicting the work accordingly wouldn’t be possible in this case.
Outliers are the units of work that take significantly longer than the average. PR cycle time metric is more reliable than the velocity in spotting outliers and anomalies in software development. It is because it measures the time it takes to complete a single unit of work. Therefore, PR cycle time helps in knowing the specific causes of delays in work.
Moreover, it also helps in getting granular insights into the development process. Hence, allowing your engineering team to improve their performance.
Among velocity and PR cycle time metrics, only the latter is directly related to business outcomes. It is a useful metric that determines how fast you can ship value to your customers; allowing you to improve speed and their planning accurately.
Moreover, cycle time is a great metric for continuously improving your team’s ability to iterate quickly. As it can help in spotting bottlenecks, inefficiencies, and areas of improvement in their processes.
Measuring cycle time using Jira or other project management tools is a manual and time-consuming process, which requires reliable data hygiene to deliver accurate results. Unfortunately, most engineering leaders have insufficient visibility and understanding of their teams’ cycle time.
Typo provides instantaneous cycle time measurement for both your organization and each development team using your Git provider.
Our methodology divides cycle time into four phases:
The subsequent phase involves analyzing the various aspects of your cycle time, including the organizational, team, iteration, and even branch levels. For instance, if an iteration has an average review time of 47 hours, you will need to identify the branches that are taking longer than usual and work with your team to address the reasons for the delay.
PR cycle time shouldn’t be the sole metric to measure software development productivity. If you do so, it would mean compromising other aspects of the software development product. Hence, you can balance it with other metrics such as DORA metrics (Deployment frequency, Lead time for change, Change failure rate and Time to restore service) too.
You can familiarize yourself with the SPACE framework when thinking about metrics to adopt in your organization. It is a research-based framework that combines quantitative and qualitative aspects of the developer and the surroundings to give a holistic view of the software development process.
At Typo, we consider the above-mentioned metrics to measure the efficiency and effectiveness of software engineering teams. Through these metrics, you can gain real-time visibility into SDLC metrics, identify bottlenecks and drive continuous improvements.
Imagine having a powerful tool that measures your software team’s efficiency, identifies areas for improvement, and unlocks the secrets to achieving speed and stability in software development – that tool is DORA metrics.
DORA metrics offer valuable insights into the effectiveness and productivity of your team. By implementing these metrics, you can enhance your dev practices and improve outcomes.
In this blog, we will delve into the importance of DORA metrics for your team and explore how they can positively impact your software team’s processes. Join us as we navigate the significance of these metrics and uncover their potential to drive success in your team’s endeavors.
DevOps Research and Assessment (DORA) metrics are a compass for engineering teams striving to optimize their development and operations processes.
In 2015, The DORA team was founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren to evaluate and improve software development practices. The aim is to enhance the understanding of how development teams can deliver software faster, more reliably, and of higher quality.
Software teams use DORA DevOps metrics in an organization to help improve their efficiency and, as a result, enhance the effectiveness of company deliverables. It is the industry standard for evaluating dev teams and allows them to scale.
The key DORA metrics include deployment frequency, lead time for changes, mean time to recovery, and change failure rate. They have been identified after six years of research and surveys by the DORA team.
To achieve success with DORA metrics, it is crucial to understand them and learn the importance of each metric. Here are the four key DORA metrics:
Implementing DORA Metrics to Improve Dev Performance & Productivity?
Organizations need to prioritize code deployment frequency to achieve success and deliver value to end users. However, it’s worth noting that what constitutes a successful deployment frequency may vary from organization to organization.
Teams that underperform may only deploy monthly or once every few months, whereas high-performing teams deploy more frequently. It’s crucial to continuously develop and improve to ensure faster delivery and consistent feedback. If a team needs to catch up, implementing more automated processes to test and validate new code can help reduce recovery time from errors.
In a dynamic market, agility is paramount. Deployment Frequency measures how frequently code is deployed. Infrequent deployments can cause you to lag behind competitors. Increasing Deployment Frequency facilitates more frequent rollouts, hence, meeting customer demands effectively.
This metric measures the time it takes to implement changes and deploy them to production directly impacts their experience, and this is the lead time for changes.
If we notice longer lead times, which can take weeks, it may indicate that you need to improve the development or deployment pipeline. However, if you can achieve lead times of around 15 minutes, you can be sure of an efficient process. It’s essential to monitor delivery cycles closely and continuously work towards streamlining the process to deliver the best experience for customers.
Picture your software development team tasked with a critical security patch. Measuring Lead Time for Changes helps pinpoint the duration from code commit to deployment. If it goes for a long run, bottlenecks in your CI/CD pipeline or testing processes might surface. Streamlining these areas ensures rapid responses to urgent tasks.
The change failure rate measures the code quality released to production during software deployments. Achieving a lower failure rate than 0-15% for high-performing DevOps teams is a compelling goal that drives continuous improvement in skills and processes. Establishing failure boundaries tailored to your organization’s needs and committing to reducing the failure rate is essential. By doing so, you enhance your software solutions and deliver exceptional user experiences.
Stability is pivotal in software deployment. The change Failure Rate measures the percentage of changes that fail. A high failure rate could signify inadequate testing or insufficient quality control. Enhancing testing protocols, refining code reviews, and ensuring thorough documentation can reduce the failure rate, enhancing overall stability.
Mean Time to Recover (MTTR) measures the time to recover a system or service after an incident or failure in production. It evaluates the efficiency of incident response and recovery processes. Optimizing MTTR aims to minimize downtime by resolving incidents through production changes. The goal is to build robust systems that can detect, diagnose, and rectify problems. Organizations ensure minimal disruption and work towards continuous improvement in incident resolution.
Downtime can be detrimental, impacting revenue and customer trust. MTTR measures the time taken to recover from a failure. A high MTTR indicates inefficiencies in issue identification and resolution. Investing in automation, refining monitoring systems, and bolstering incident response protocols minimizes downtime, ensuring uninterrupted services.
Teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.
A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.
Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.
Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.
Rapid post-review commits and a high deployment frequency reflect agile responsiveness to feedback. This iterative approach, driven by quick feedback incorporation, yields reliable releases, fostering customer trust and satisfaction.
Despite few post-review commits, high deployment frequency signals comprehensive pre-submission feedback integration. Emphasizing thorough code reviews assures stable deployments, showcasing the team’s commitment to quality.
Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.
A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.
In collaborative software development, optimizing code collaboration efficiency is paramount. By analyzing Comments per PR (reflecting review depth) alongside Commits after PR is Raised for Review, teams gain crucial insights into their code review processes.
Thorough reviews with limited code revisions post-feedback indicate a need for iterative development. Encouraging developers to iterate fosters a culture of continuous improvement, driving efficiency and learning.
Few comments during reviews paired with significant post-review commits highlight the necessity for robust initial reviews. Proactive engagement during the initial phase reduces revisions later, expediting the development cycle.
The size of pull requests (PRs) profoundly influences deployment timelines. Correlating Large PR Size with Deployment Frequency enables teams to gauge the effect of extensive code changes on release cycles.
Maintaining a high deployment frequency with substantial PRs underscores effective testing and automation. Acknowledge this efficiency while monitoring potential code intricacies, ensuring stability amid complexity.
Infrequent deployments with large PRs might signal challenges in testing or review processes. Dividing large tasks into manageable portions accelerates deployments, addressing potential bottlenecks effectively.
PR size significantly influences code quality and stability. Analyzing Large PR Size alongside Change Failure Rate allows engineering leaders to assess the link between PR complexity and deployment stability.
Frequent deployment failures with extensive PRs indicate the need for rigorous testing and validation. Encourage breaking down large changes into testable units, bolstering stability and confidence in deployments.
A minimal failure rate with substantial PRs signifies robust testing practices. Focus on clear team communication to ensure everyone comprehends the implications of significant code changes, sustaining a stable development environment. Leveraging these correlations empowers engineering teams to make informed, data-driven decisions — a great way to drive business outcomes— optimizing workflows, and boosting overall efficiency. These insights chart a course for continuous improvement, nurturing a culture of collaboration, quality, and agility in software development endeavors.
In the ever-evolving world of software development, harnessing the power of DORA DevOps metrics is a game-changer. By leveraging DORA key metrics, your software teams can achieve remarkable results. These metrics are an effective way to enhance customer satisfaction, mitigate financial risks, meet service-level agreements, and deliver high-quality software.
Implementing DORA Metrics to Improve Dev Performance & Productivity?
“This article is an amazing eye-opener for many engineering leaders on how to use DORA metrics. Correlating metrics gives the real value in terms of SDLC insights and that's what is the need of the hour."
“That is the ultimate goal - connecting DevOps to DORA. Super helpful article for teams looking at implementing DORA.”
Numerous metrics are available for monitoring software development progress and generating reports that indicate the performance of your engineering team can be a time-consuming task, taking several hours or even days.Through our own research and collaboration with industry experts like DORA, we suggest concentrating on cycle time, also referred to as a lead time for changes, which we consider the most crucial metric to monitor.This measurement indicates the performance and efficiency of your teams and developers. In this piece, we will cover what cycle time entails, its significance, methods for calculating it, and actions to enhance it.
Cycle Time in software development denotes the duration between an engineer’s first commit and code deployment, which some teams also refer to as lead time. This measurement indicates the time taken to finalize a specific development task. Cycle time serves as a valuable metric for deducing a development team’s process speed, productivity, and capability of delivering functional software within a defined time frame.
Leaders who measure cycle time gain insight into the speed of each team, the time taken to finish specific projects, and the overall performance of teams relative to each other and the organization. Moreover, optimizing cycle time enhances team culture and stimulates innovation and creativity in engineering teams.
However, cycle time is a lagging indicator, implying that it confirms ongoing patterns rather than measures productivity. As such, it can be utilized as a signal of underlying problems within a team.
Since cycle time reflects the speed of team performance, most teams aim to maintain low cycle times that enhance their efficiency. According to the Accelerate State of DevOps Report research, the top 25% of successful engineering teams achieve a cycle time of 1.8 days, while the industry-wide median cycle time is 3.4 days. On the other hand, the bottom 25% of teams have a cycle time of 6.2 days.
Measuring cycle time using Jira or other project management tools is a manual and time-consuming process, which requires reliable data hygiene to deliver accurate results. Unfortunately, most engineering leaders have insufficient visibility and understanding of their teams' cycle time.Typo provides instantaneous cycle time measurement for both your organization and each development team using your Git provider.Our methodology divides cycle time into four phases:
The subsequent phase involves analyzing the various aspects of your cycle time, including the organizational, team, iteration, and even branch levels. For instance, if an iteration has an average review time of 47 hours, you will need to identify the branches that are taking longer than usual and work with your team to address the reasons for the delay.
Although managers and leaders are aware of the significance of cycle time, they aren't necessarily armed with the information necessary to understand why the cycle time of their team may be higher or lower than ideal. Leaders may make decisions that have a beneficial impact on developer satisfaction, productivity, and team performance by understanding the processes that make up cycle time and exploring its constituent parts.Cycle time could increase as engineers wait for builds to finish and tests to pass before the PR is ready for review. When engineers must make modifications following each review and wait for a drawn-out and delayed CI/CD that extends the time to merge, the process becomes even more wasteful. This not only lengthens the cycle time but also causes contributors to feel frustrated.
The time it takes to open a PR increases because large-sized PRs take longer to code and, as a result, stay closed for too long. For instance, the majority of teams aim for PR sizes to be under 300 changes, and as this limit rises, the time to open the PR lengthens. Even when huge PRs are opened, they are often not moved to the code review stage because most reviewers are reluctant to do so for the following two reasons:
A high PR indicates that the reviewer put a lot of effort into the review. To accommodate a significant code review, the reviewer must plan and significantly restructure their current schedule. It takes heavy and intense effort.
Huge PRs are notorious for their capacity to add a number of new bugs.
Code comments and other forms of documentation in the code are best practices that are regrettably frequently ignored. Reviewers and future collaborators can evaluate and work on code more quickly and effectively with the use of documentation, cutting down on pickup time and rework time. Coding standards assist authors in starting off with pull requests that are in better shape. They also assist reviewers in avoiding repeated back and forth on fundamental procedures and standards. When working on code that belongs to other teams, this documentation is very useful for cross-team or cross-functional collaboration. Various teams adhere to various coding patterns, and consistency is maintained by documentation.
Teams can greatly benefit from a readme that is relevant to a codebase and contains information about coding patterns, and supporting materials, such as how and where to add logs, coding standards, emit metrics, approval requirements, etc.
Cycle time could increase as engineers wait for builds to finish and tests to pass before the PR is ready for code review. When engineers must make modifications following each review and wait for a drawn-out and delayed CI/CD that extends the time to merge, the process becomes even more wasteful. This not only lengthens the cycle time but also causes contributors to feel frustrated. Moreover, when the developers don't adhere to coding standards before entering the CI/CD pipeline can increase cycle time and reduce code quality.
Engineers may struggle with numerous WIP PRs due to an unmanaged and heavy workload, in turn reporting lengthier coding and rework times. Reviewers are more likely to become overburdened by the sheer volume of review requests at the end of a sprint than by a steady stream of PRs. This limits reviewers’ own coding time as their coding skills start deteriorating and causes a large number of PRs to be merged without review, endangering the quality of the code.
The team experiences a high cycle time as reviewers struggle to finish their own code, the reviews, and the rework, and they suffer burnout.
When teams fail to perform simple sanity checks and debugging needs before creating PRs (such as linting, test code coverage, and initial debugging), it results in avoidable nitpicks during a code review (where the reviewer may be required to spend time pointing out formatting errors or test coverage thresholds that the author should have covered by default).
So, now that you're confidently tracking cycle time and all four phases, what can you do to make your engineering organization's cycle time more consistent and efficient? How can you reap the benefits of good developer experience, efficiency, predictability, and keeping your business promises?
Start measuring the cycle time and breakdown in four phases in real-time. Start comparing the benchmarks with the industry standards.
Once you’ve benchmarked your cycle time and all four phases, you’ll know which areas are causing bottlenecks and require attention. Then everyone in your organisation will be on the same page about how to effectively reduce cycle time.
We recommend that you focus on one or two bottlenecks at a time—for example, PR size and review time—and design your improvement strategy around them.
Bring past performance data to your next retro to help align the team. Using engineering benchmarks, provide context into performance. Then, over the next 2-3 iterations, set goals to improve one tier.
We also recommend that you develop a cadence for tracking progress. You could, for example, repurpose an existing ceremony or make time specifically for goals.
Build an alert system to reduce the cycle time by utilizing Slack to assist developers in navigating a growing PR queue.
These pieces of data enable the developer to make more informed decisions. They respond to questions such as: Do I have enough time for this review during my next small break, or should I queue it?
Many organizations are adopting agile methodologies. As they help in prioritizing continuous feedback, iterative development, and team collaboration. By adopting these practices, the team can leverage their coding skills and divide large programming tasks into small, manageable chunks. Hence, completing them in a shorter cycle to enable faster delivery times.
The most successful teams are those that have mastered the entire coding-to-deployment process and can consistently provide new value to customers.Measuring your development workflow with typo’s Engineering Benchmarks and automating improvement with Team Goals and our Slack alerts will enable your team to build and ship features more quickly while increasing developer experience and quality.
Consider a world where metrics and dashboards do not exist, where your work is free from constraints and you have the freedom to explore your imagination, creativity, and innovative ideas without being tethered to anything.
It may sound like a utopian vision that anyone would crave, right? But, it is not a sentiment shared by business owners and managers. They operate in a world where OKRs, KPIs, and accountability define performance. In this environment, dreaming and fairy tales have no place.
Given that distributed teams are becoming more prevalent and the demand for rapid development is skyrocketing, managers seek ways to maintain control. Managers have started favoring “DORA metrics” to achieve this goal in development teams. By tracking and trying to enhance these metrics, managers feel as though they have some degree of authority over their engineering team’s performance and culture.
But, here’s a message for all the managers out there on behalf of developers - DORA DevOps metrics alone are insufficient and won’t provide you with the help you require.
Before we understand, why DORA is insufficient today, let’s understand what are they!
The widely used reference book for engineering leaders called Accelerate introduced the DevOps Research and Assessment (DORA) group's four metrics, known as the DORA 4 metrics.
These metrics were developed to assist engineering teams in determining two things: A) The characteristics of a top-performing team, and B) How their performance compares to the rest of the industry.
The four key DORA metrics are as follows:
Deployment Frequency measures the frequency of code deployment to production or releases to end-users in a given time frame. It may include the code review consideration as it assesses code changes before they are integrated into a production environment.
It is a powerful driver of agility and efficiency that makes it an essential component of software development. High deployment frequency results in rapid releases without compromising the software's robustness, hence, enhancing customer satisfaction.
This metric measures the time between a commit being made and that commit making it to production. It helps to understand the effectiveness of the development process once coding has been initiated.
A shorter lead time signifies the DevOps teams are efficient in deploying code while a longer lead time means the testing process is obstructing the CI/CD pipeline. Hence, differentiating elite performers from low performers.
This metric is also known as the mean time to restore. It measures the time required to solve the incident i.e. service incident or defect impacting end-users. To lower it, the team must improve their observation skills so that failures can be detected and resolved quickly.
Minimizing MTTR enhances user satisfaction and mitigates the negative impacts of downtime on business operations.
Change failure rate measures the proportion of deployment to production that results in degraded services. It should be kept as low as possible as it will signify successful debugging practices and thorough testing and problem-solving.
Lowering CFR is a crucial goal for any organization that wants to maintain a dependable and efficient deployment pipeline. A high change failure rate can have serious consequences, such as delays, rework, customer dissatisfaction, revenue loss, or even security breaches.
In their words:
“Deployment Frequency and Lead Time for Changes measure velocity, while Change Failure Rate and Time to Restore Service measure stability. And by measuring these values, and continuously iterating to improve on them, a team can achieve significantly better business outcomes.”
Below are the performance metrics categorized in
for 4 metrics –
DORA metrics are a useful tool for tracking and comparing DevOps team performance. Unfortunately, it doesn't take into account all the factors for a successful software development process. For example, assessing coding skills across teams can be challenging due to varying levels of expertise. These metrics also overlook the actual efforts behind the scenes, such as debugging, feature development, and more.
While DORA metrics tell us which metric is low or high, it doesn't reveal the reason behind it. Suppose, there is an increase in lead time for changes, it could be due to various reasons. For example, DORA metrics might not reflect the effectiveness of feedback provided during code review. Hence, overlooking the true impact and value of the code review process.
The software development landscape is changing rapidly. Hence, the DORA metrics may not be able to quickly adapt to emerging programming practices, coding standards, and other software trends. For instance, Code review has evolved to include not only traditional peer reviews but also practices like automated code analysis. DORA metrics may not be able to capture the new approaches fully. Hence, it may not be able to assess the effectiveness of these reviews properly.
DORA metrics are a great tool for analyzing DevOps performance. But, It doesn't mean they are relevant to every developer's team. These key metrics work best when the deployment is done frequently, can quickly iterate on changes, and improve accordingly. For example, if your team adheres to certain coding standards or ship software monthly, it will result in low deployment frequency almost every time and helps to deliver high-quality software.
Relying solely on DORA metrics to evaluate software teams' performance has limited value. Leaders must now move beyond these metrics, identify patterns, and obtain a comprehensive understanding of all factors that impact the software development life cycle (SDLC).
For example, if a team's cycle time varies and exceeds three days, while all other metrics remain constant, managers must investigate deployment issues, the time it takes for pull requests to be approved, the review process, or a decrease in a developer's productivity.
If a developer is not coding as many days, what is the reason behind this? Is it due to technical debt, frequent switching between tasks, or some other factor that hasn't yet been identified? Therefore, leaders need to look beyond the DORA metrics and understand the underlying reasons behind any deviations or trends in performance.
For DORA to produce reliable results, software development teams must have a clear understanding of the metrics they are using and why they are using them. DORA can provide similar results for teams with similar deployment patterns. But, it is also essential to use the data to advance the team’s performance rather than simply relying on the numbers. Combining DORA with other engineering analytics is a great way to gain a complete picture of the development process. It may include identifying bottlenecks and improvement areas. It may include including identifying bottlenecks and improvement areas.
However, poor interpretation of DORA data can occur due to the lack of uniformity in defining failure, which is a challenge for metrics like CFR and MTTR. Using custom information to interpret the results is often ineffective. Additionally, DORA metrics only focus on velocity and stability. It does not consider other factors such as the quality of work, productivity of developers, and the impact on the end-user. So, it is important to use other indexes for a proactive response, qualitative analysis of workflows, and SDLC predictability. It will help to gain a 360-degree profiling of the team’s workflow.
To achieve business goals, it is essential to correlate DORA data with other critical indicators like review time, code churn, maker time, PR size, and more. Using DORA in combination with more context, customization, and traceability can offer valuable insights and a true picture of the team’s performance and identify the steps needed to resolve bottlenecks and hidden fault lines at all levels. Ultimately, DORA should be used as a tool for continuous improvement, product management, and enhancing value delivery.
DORA metrics can also provide insights into coding skills by revealing patterns related to code quality, review effectiveness, and debugging cycles. This can help to identify the blind spots where additional training is required.
Typo is a powerful engineering analytics tool for tracking and analyzing DORA metrics. It provides an efficient solution for software development teams seeking precision in their DevOps performance measurement and delivers high-quality software to end users.
While DORA serves its purpose well, it is only the beginning of improving engineering excellence. To effectively measure DORA metrics, it is essential to focus on key DORA metrics and the business value they provide. Looking at numbers alone is not enough. Engineering managers should also focus on the practices and people behind the numbers and the barriers they face to achieve their best and ensure customer satisfaction. It is a known fact that engineering excellence is related to a team’s productivity and well-being. So, an effective way is to consider all factors that impact a team’s performance and take appropriate steps to address them.
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