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!

Timestamps

• 00:00 - Introduction

• 00:59 - Meet Richard Pangborn

• 02:58 - Meet Bryan Finster

• 04:49 - Bryan's Journey with Continuous Delivery

• 07:33 - Challenges & Misuse of DORA Metrics

• 20:55 - Richard's Experience with DORA Metrics

• 27:43 - Ownership of MTTR & Measurement Challenges

• 28:27 - Cultural Resistance to Measurement

• 29:37 - Team Metrics vs Individual Metrics

• 31:29 - Value Stream Mapping Insights

• 33:56 - Q&A Session

• 40:19 - Setting Realistic Goals with DORA Metrics

• 45:31 - Final Thoughts & Advice

Links and Mentions 

• Richard Pangborn on Linkedin

• Bryan Finster on Linkedin

• Kovid Batra on Linkedin

• Watch this webinar on YouTube and Substack

• Listen on Spotify, Amazon Music, and iTunes/Apple Podcasts

Transcript

‍

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.

What is Jellyfish? 

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. 

‍

Top Jellyfish Alternatives 

Typo 

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. 

Price

• Free: $0/dev/month

• Starter: $16/dev/month

• Pro: $24/dev/month

• Enterprise: Quotation on request

‍

LinearB 

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. 

Price

• Free: $0/dev/month

• Business: $49/dev/month

• Enterprise: Quotation on request

‍

‍

Waydev

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.

Price

• Quotation on request

‍

‍

Pluralsight Flow 

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.

Price

• Core: $38/mo

• Plus: $50/mo

‍

Code Climate Velocity

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.

Price

• Open Source: $0 (Free forever)

• Startup: $0: Up to 4 seats

• Team: $16.67/month/seat billed annually ($20 billed monthly)

‍

Swarmia 

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. 

‍

Price

• Free: 0£/dev/month

• Lite: 20£/dev/month

• Standard: 39£/dev/month

‍

Conclusion 

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.

What is Cycle Time?

Cycle time is a crucial metric that measures the average time PR spends in all stages of the development pipeline. These stages are: 

• The Coding time represents the time taken to write and complete the code changes.

• The Pickup time denotes the time spent before a pull request is assigned for review.

• The Review time encompasses the time taken for peer review and feedback on the pull request.

• The Merge time shows the duration from the approval of the pull request to its integration into the main codebase.

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. 

Why Measuring Cycle Time Matters? 

• PR cycle time allows software development teams to understand how efficiently they are working. Low cycle time indicates a faster review process and quick integrations of code changes, leading to a high level of efficiency. 

• Measuring cycle time helps to identify stages in the development process where work is getting stuck or delayed. This allows teams to pinpoint bottlenecks and areas that require attention. 

• Monitoring PR Cycle Time regularly informs process improvements. Hence, helping teams create and implement more effective and streamlined workflows.

• Cycle time fosters continuous improvement. This enables them to adapt to changing requirements more quickly, maintain a high level of productivity, and ship products faster. 

• Cycle time allows better forecasting and planning which allows engineering teams to estimate project timelines regularly and manage stakeholder expectations.  

What is PR Review Time? 

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 - 

‍

‍

How to Identify High Review Time?

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. 

‍

‍

Causes of High Review Time

Unawareness of the PR being issued

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.

Large PRs

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.

Team is diverted to other work

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.

Too much WIP

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.

Too few people are assigned to do reviews

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.

Ways to Reduce Review Time

Here are some steps on how you can monitor and reduce your review time

Set Goals for the 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. 

‍

‍

Focus on high-priority items

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.

Regular code reviews 

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.

Create standards and guidelines 

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  - 

• PR merged w/o review

• Pickup time

• PR size

Effective communication 

Ensure that there is clear communication among the development team and stakeholders to quickly identify issues and resolve them timely. 

Conduct peer reviews 

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.

Conclusion

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.

What are DORA Metrics?

DORA metrics are a set of four key metrics that measure the efficiency and effectiveness of your software delivery process:

• Deployment Frequency: This metric measures how often your team successfully releases new features or fixes to production.

• Lead Time for Changes: This metric measures the average time it takes for a code change to go from commit to production.

• Change Failure Rate: This metric measures the percentage of deployments that result in production incidents.

• Mean Time to Restore (MTTR): This metric measures the average time it takes to recover from a production incident.

Why are DORA Metrics Important?

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.

How to Achieve Elite Levels of DORA Metrics

Here are some key strategies to achieve elite levels of DORA metrics:

• Embrace a Culture of Continuous Delivery:
  A culture of continuous delivery emphasizes automating the software delivery pipeline. This allows for faster and more frequent deployments with lower risk.

• Invest in Automation:
  Automating manual tasks in your delivery pipeline can significantly reduce lead times and improve deployment frequency. This includes automating tasks such as testing, building, and deployment.

• Break Down Silos:
  Effective collaboration between development, operations, and security teams is essential for high performance. Break down silos between these teams to foster a shared responsibility for delivery.

• Implement Continuous Feedback Loops:
  Establish feedback loops throughout your delivery pipeline to identify and fix issues early. This can involve practices like code reviews, automated testing, and performance monitoring.

• Focus on Error Prevention:
  Shift your focus from fixing errors in production to preventing them from occurring in the first place. Utilize tools and techniques like static code analysis and unit testing to catch errors early in the development process.

• Measure and Monitor:
  Continuously track your DORA metrics to identify trends and measure progress. Use data-driven insights to guide your improvement efforts.

• Promote a Culture of Learning:
  Create a culture of continuous learning within your team. Encourage team members to experiment with new technologies and approaches to improve delivery performance.

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.

Additional Tips

In addition to the above strategies, here are some additional tips for achieving elite DORA metrics:

• Set clear goals for your DORA metrics and track your progress over time.

• Communicate your DORA metrics goals to your entire team and get everyone on board.

• Celebrate successes and milestones along the way.

• Continuously seek feedback from your team and stakeholders and adapt your approach as needed.

By following these tips and focusing on continuous improvement, you can help your DevOps team reach new heights of performance.

Leveraging LLM Models to Achieve DevOps Excellence

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.

Specific Use Cases for LLMs in DevOps

Code Generation and Review:

• Autogenerate boilerplate code, unit tests, or even entire functions based on natural language descriptions.

• Assist in code reviews by suggesting improvements, identifying potential issues, and enforcing coding standards.

Incident Response and Root Cause Analysis:

• Analyze log files, error messages, and monitoring data to swiftly identify the root cause of incidents.

• Generate incident reports and suggest remediation steps.

Documentation Generation:

• Create and maintain up-to-date documentation for codebases, infrastructure, and processes.

• Generate API documentation, user manuals, and knowledge bases.

‍Predictive Analytics:

• Analyze historical data to forecast potential issues, such as infrastructure bottlenecks or application performance degradation.

• Provide early warnings to prevent service disruptions.

Chatbots and Virtual Assistants:

• Develop intelligent chatbots to provide support to developers and operations teams.

• Automate routine tasks and answer frequently asked questions.

Natural Language Querying of DevOps Data:

• Allow users to query DevOps metrics and data using natural language.

• Generate insights and visualizations based on user queries.

Automation Scripting:

• Assist in generating scripts for infrastructure provisioning, configuration management, and deployment automation.

• Improve automation efficiency and reduce human error.

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.

What is Cycle Time?

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.

• The Coding time represents the time taken to write and complete the code changes.

• The Pickup time denotes the time spent before a pull request is assigned for review.

• The Review time encompasses the time taken for peer review and feedback on the pull request.

• The Merge time shows the duration from the approval of the pull request to its integration into the main codebase.

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.

Why Measuring Cycle Time Improves Engineering Efficiency? 

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:

• Measuring cycle time for individual tasks or user stories can identify stages in the development process where work tends to get stuck or delayed. This helps to pinpoint bottlenecks and areas that need improvement.

• Cycle time indicates the overall efficiency of your development process. Shorter cycle times generally reflect a streamlined and efficient workflow.

• Understanding cycle time helps with better forecasting and planning. Knowing how long it typically takes to complete tasks can accurately estimate project timelines and manage stakeholder expectations.

• Measuring cycle time allows you to evaluate the impact of process changes. 

• Effective cycle time data for individual team members provides insights into their productivity and can be used for performance evaluations.

• Tracking cycle time across multiple projects or teams allows process standardization and best practice identification.

What is Coding Time? 

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.

How to Identify High-Coding Time?

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. 

Causes of High Coding Time

There are broadly three main causes of high coding time

• The task is too large on its own

• Task requirements need clarification

• Too much work in progress

The Task is Too Large

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” 

Task Requirements Need clarification

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.

Too Much Work in Progress

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.

‍

Ways to Prevent High Coding Time

Set up Slack Alerts for High-Risk Work

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.

Balance Workload in the Team

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.

Use a Framework

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

Code Reuse

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

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.

Use Agile Methodologies

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

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.

Conclusion

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.

What is Waydev?

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. 

‍

Top Waydev Alternatives 

A few of the best Waydev alternatives are: 

Typo 

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.

‍

Price:

• Free: $0/dev/month

• Starter: $16/dev/month

• Pro: $24/dev/month

• Enterprise: Quotation on request

LinearB

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.‍ 

‍

Price:

• Free: $0/dev/month

• Business: $49/dev/month

• Enterprise: Quotation on request

Jellyfish 

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. 

‍

Price

• Quotation on request

Swarmia 

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.

‍

Price

• Free: 0£/dev/month

• Lite: 20£/dev/month

• Standard: 39£/dev/month

Pluralsight Flow

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.

‍

Price

• Core: $38/mo

• Plus: $50/mo

How to Select the Right Software Development Analytics Tool for your Team?

Picking the right analytics tool is important for the software engineering team. Check out these essential factors below before you make a purchase:

Scalability

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

Error detection feature must be present in the analytics tool as it helps to improve code maintainability, mean time to recovery, and bug rates.

Security Capability

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.

Ease of Use

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.

Integrations

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.

Conclusion

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.

What are DevOps Metrics and KPIs? 

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.

Why Measure DevOps Metrics and KPIs? 

Measuring DevOps metrics and KPIs is beneficial for various reasons:

• DevOps Metrics help identify areas where processes may be inefficient or problematic. Hence, enabling teams to address issues and optimize performance.

• Tracking metrics allows development teams to maintain high standards for software quality and reliability.

• They provide a basis for evaluating the effectiveness of DevOps practices and making data-driven decisions to drive continuous improvement and enhance processes.

• KPIs ensure that DevOps efforts are aligned with broader business objectives. This allows organizations to achieve strategic goals and deliver value to the end-users. 

• They provide visibility into the DevOps process that fosters better communication and collaboration within DevOps teams. 

• Measuring metrics continuously allows teams to monitor progress, set benchmarks, and assess the impact of changes and improvements.

• They help make strategic decisions, allowing resource utilization effectively and prioritizing initiatives based on their impact.

Key DevOps Metrics and KPIs

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

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

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. 

Change Failure Rate

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

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

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

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

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

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

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

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.

Pull Request Size

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

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

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

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.

Improve Your DevOps KPIs with Typo

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.

• Offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.
• Analyze the code coverage report within a few minutes and provide detailed coverage reports.
• Auto-analyses codebase and pull requests to find issues and auto-generates fixes before you merge to master. 
• Offers engineering benchmark to compare the team’s results across industries.
• User-friendly interface.

Conclusion

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?

Timestamps

• 00:00 - Introduction

• 02:11 - Understanding the Low Uptake of Metrics

• 08:11 - Mindset Shifts Essential for Metrics Implementation

• 10:11 - Ideal Team Size for Metrics Implementation

• 15:36 - How to Identify Benchmarks?

• 22:06 - Aligning Business with Engineering Metrics

• 25:04 - Choosing the Right Metrics

• 30:49 - Q&A Session

• 45:43 - Conclusion

Links and Mentions 

• Nathen Harvey on Linkedin

• Ido Shveki on Linkedin and Medium

• Kovid Batra on Linkedin

• Watch this webinar on Youtube and Substack

• Listen on Spotify, Amazon Music and Apple Podcasts

Webinar Transcript

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. 

‍

‍What are Software Development Metrics?

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.

Importance of Software Development Metrics:

• Software engineering metrics evaluate the productivity and efficiency of development teams, ensuring that projects are progressing as planned.

• These metrics ensure that the projects are progressing as planned and potential bottlenecks are taken into consideration as early as possible.

• Software quality metrics help to identify areas for improving software quality and stability.

• These metrics monitor progress, manage timelines, and enable software developers to make informed decisions about project scope and deadlines.

• Regular reviewing and analysis of metrics allow team members to identify weaknesses and optimize processes for better performance and efficiency.

• Metrics assist in understanding resource utilization which leads to better allocation and management of development resources.

• Software engineering metrics related to user feedback and satisfaction ensure that the software meets user needs and expectations and drives enhancements based on actual user experience.

Process Metrics

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

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

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.

Cycle Time

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

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. 

‍

Performance Metrics

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

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

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

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

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

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

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 

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 in Progress (WIP) 

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 

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

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 Metrics

Test Coverage

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.

Defect Density

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.

Test Automation Rate

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.

Productivity Metrics

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:

Code Review Time

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

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

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

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

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.

Mean Time Between Failures (MTBF)

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 

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 (MTTD) 

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. 

Number of Vulnerabilities 

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

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. 

Conclusion

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.

What are DORA Metrics?

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.

Four Key Metrics

• Deployment Frequency measures how often code is deployed into production per week.

• Lead Time for Changes measures the time it takes for code changes to move from inception to deployment.

• Change Failure Rate measures the code quality released to production during software deployments.

• Mean Time to Recover measures the time to recover a system or service after an incident or failure in production.

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.

Signs leading to Poor DORA Metrics

Deployment Frequency

Deployment Frequency measures how often a team deploys code to production. Symptoms affecting this metric include:

• High Rework Rate -  Frequent modifications to deployed code can delay future deployments as teams focus on fixing issues.

• Oversized Pull Requests -  Large pull requests can complicate the review process, causing delays in deployment.

• Manual Deployment Processes -  Reliance on manual steps can introduce errors and slow down the release cycle.

• Poor Test Coverage -  Insufficient automated testing can lead to hesitancy in deploying changes, impacting frequency.

• Low Team Morale -  Frustration from continuous issues can reduce motivation to deploy frequently.

• Lack of Clear Objectives -: Unclear goals leads misalignment and wasted efforts which hinders deployment frequency.

• Inefficient Branching Strategy -  A poorly designed branching strategy result in merge conflicts, integration issues, and delays in merging changes into the main branch which further impacts deployment frequency.

• Inadequate Monitoring and Observability -  Lack of effective monitoring and observability tools can make it difficult to identify and troubleshoot issues in production. 

Lead Time for Changes 

Lead Time for Changes measures the time taken from code commit to deployment. Symptoms impacting this metric include:

• High Technical Debt - Accumulated technical debt can complicate code changes, extending lead times.

• Inconsistent Code Review Practices -  Variability in review quality can lead to delays in approval and testing.

• High Cognitive Load -  Overloaded team members may struggle to focus, leading to slower progress on changes.

• Frequent Context Switching - Team members shifting focus between tasks can increase lead time due to lost productivity.

• Poor Communication -  Lack of collaboration can result in misunderstandings and delays in the development process.

• Unclear Requirements -  Ambiguity in project requirements can lead to rework and extended lead times.

• Inefficient Issue Tracking -  Poorly managed issue tracking systems can lead to lost or forgotten tasks, duplicated efforts, and delays in addressing issues, ultimately extending lead times.

• Lack of Automated Testing -  Insufficient automated testing can lead to manual testing bottlenecks, delaying the integration and deployment of changes.

Change Failure Rate

Change Failure Rate indicates the percentage of changes that result in failures. Symptoms affecting this metric include:

• Poor Test Coverage -  Insufficient testing increases the likelihood of bugs in production.

• High Pull Request Revert Rate -  Frequent rollbacks suggest instability in the codebase, indicating a high change failure rate.

• Lightning Pull Requests -  Rapid submissions without adequate review can introduce errors and increase failure rates

• Inadequate Incident Response Procedures -  Poorly defined processes can lead to higher failure rates during deployments.

• Knowledge Silos -  Lack of shared knowledge within the team can lead to mistakes and increased failure rates.

• High Code Quality Bugs - Frequent bugs in the code can indicate underlying quality issues, raising the change failure rate.

• Lack of Feature Flags -  The absence of feature flags can make it difficult to roll back changes or experiment with new features, increasing the risk of failures in production.

• Insufficient Monitoring and Alerting - Inadequate monitoring and alerting systems can make it challenging to detect and respond to issues in production, leading to prolonged failures and increased change failure rates.

Mean Time to Restore Service

Mean Time to Restore Service measures how long it takes to recover from a failure. Symptoms impacting this metric include:

• High Technical Debt -  Complexity in the codebase can slow down recovery efforts, extending MTTR.

• Recurring High Cognitive Load -  Overburdened team members may take longer to diagnose and fix issues.

• Poor Documentation -  Lack of clear documentation can hinder recovery efforts during incidents.

• Inconsistent Incident Management -  Variability in handling incidents can lead to longer recovery times.

• High Rate of Production Incidents -  Frequent issues can overwhelm the team, extending recovery times.

• Lack of Post-Mortem Analysis -  Not analyzing incidents can prevent learning from failures, which can result in repeated issues and longer recovery times.

• Insufficient Automation - Lack of automation in incident response and remediation processes causes manual, time-consuming troubleshooting, extending recovery times.

• Inadequate Monitoring and Observability -  Insufficient monitoring and observability tools can make it difficult to quickly identify and diagnose issues in production which further delay the restoration of service.

• Siloed Incident Response -  Lack of cross-functional collaboration and communication during incidents lead to delays in restoring service. As team members may not have a complete understanding of the issue or the necessary context to resolve it swiftly. 

Improve your DORA Metrics using Typo

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.

Conclusion

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.

‍

Introduction

In today's fast-paced and rapidly evolving software development landscape, effective project management is crucial for engineering teams striving to meet deadlines, deliver quality products, and maintain customer satisfaction. Project management not only ensures that tasks are completed on time but also optimizes resource allocation enhances team collaboration, and improves communication across all stakeholders. A key tool that has gained prominence in this domain is JIRA, which is widely recognized for its robust features tailored for agile project management.

However, while JIRA offers numerous advantages, such as customizable workflows, detailed reporting, and integration capabilities with other tools, it also comes with limitations that can hinder its effectiveness. For instance, teams relying solely on JIRA dashboard gadget may find themselves missing critical contextual data from the development process. They may obtain a snapshot of project statuses but fail to appreciate the underlying issues impacting progress. Understanding both the strengths and weaknesses of JIRA dashboard gadget is vital for engineering managers to make informed decisions about their project management strategies.

The Limitations of JIRA Dashboard Gadgets

Lack of Contextual Data

JIRA dashboard gadgets primarily focus on issue tracking and project management, often missing critical contextual data from the development process. While JIRA can show the status of tasks and issues, it does not provide insights into the actual code changes, commits, or branch activities that contribute to those tasks. This lack of context can lead to misunderstandings about project progress and team performance. For example, a task may be marked as "in progress," but without visibility into the associated Git commits, managers may not know if the team is encountering blockers or if significant progress has been made. This disconnect can result in misaligned expectations and hinder effective decision-making.

Static Information

JIRA dashboards having road map gadget or sprint burndown gadget can sometimes present a static view of project progress, which may not reflect real-time changes in the development process. For instance, while a JIRA road map gadget or sprint burndown gadget may indicate that a task is "done," it does not account for any recent changes or updates made in the codebase. This static nature can hinder proactive decision-making, as managers may not have access to the most current information about the project's health. Additionally, relying on historical data can create a lag in response to emerging issues in issue statistics gadget. In a rapidly changing development environment, the ability to react quickly to new information is crucial for maintaining project momentum hence we need to move beyond default chart gadget like road map gadget or burndown chart gadget.

Limited Collaboration Insights

Collaboration is essential in software development, yet JIRA dashboards often do not capture the collaborative efforts of the team. Metrics such as code reviews, pull requests, and team discussions are crucial for understanding how well the team is working together. Without this information, managers may overlook opportunities for improvement in team dynamics and communication. For example, if a team is actively engaged in code reviews but this activity is not reflected in JIRA gadgets or sprint burndown gadget, managers may mistakenly assume that collaboration is lacking. This oversight can lead to missed opportunities to foster a more cohesive team environment and improve overall productivity.

Overemphasis on Individual Metrics

JIRA dashboard or other copy dashboard can sometimes encourage a focus on individual performance metrics rather than team outcomes. This can foster an environment of unhealthy competition, where developers prioritize personal achievements over collaborative success. Such an approach can undermine team cohesion and lead to burnout. When individual metrics are emphasized, developers may feel pressured to complete tasks quickly, potentially sacrificing code quality and collaboration. This focus on personal performance can create a culture where teamwork and knowledge sharing are undervalued, ultimately hindering project success.

Inflexibility in Reporting

JIRA dashboard layout often rely on predefined metrics and reports, which may not align with the unique needs of every project or team. This inflexibility can result in a lack of relevant insights that are critical for effective project management. For example, a team working on a highly innovative project may require different metrics than a team maintaining legacy software. The inability to customize reports can lead to frustration and a sense of disconnect from the data being presented.

The Power of Integrating Git Data with JIRA

Integrating Git data with JIRA provides a more holistic view of project performance and developer productivity. Here’s how this integration can enhance insights:

Real-Time Visibility into Development Activity

By connecting Git repositories with JIRA, engineering managers can gain real-time visibility into commits, branches, and pull requests associated with JIRA issues & issue statistics. This integration allows teams to see the actual development work being done, providing context to the status of tasks on the JIRA dashboard gadet. For instance, if a developer submits a pull request that relates to a specific JIRA ticket, the project manager instantly knows that work is ongoing, fostering transparency. Additionally, automated notifications for changes in the codebase linked to JIRA issues keep everyone updated without having to dig through multiple tools. This integrated approach ensures that management has a clear understanding of actual progress rather than relying on static task statuses.

Enhanced Collaboration and Communication

Integrating Git data with JIRA facilitates better collaboration among team members. Developers can reference JIRA issues in their commit messages, making it easier for the team to track changes related to specific tasks. This transparency fosters a culture of collaboration, as everyone can see how their work contributes to the overall project goals. Moreover, by having a clear link between code changes and JIRA issues, team members can engage in more meaningful discussions during stand-ups and retrospectives. This enhanced communication can lead to improved problem-solving and a stronger sense of shared ownership over the project.

Improved Risk Management

With integrated Git and JIRA data, engineering managers can identify potential risks more effectively. By monitoring commit activity and pull requests alongside JIRA issue statuses, managers can spot trends and anomalies that may indicate project delays or technical challenges. For example, if there is a sudden decrease in commit activity for a specific task, it may signal that the team is facing challenges or blockers. This proactive approach allows teams to address issues before they escalate, ultimately improving project outcomes and reducing the likelihood of last-minute crises.

Comprehensive Reporting and Analytics

The combination of JIRA and Git data enables more comprehensive reporting and analytics. Engineering managers can analyze not only task completion rates but also the underlying development activity that drives those metrics. This deeper understanding can inform better decision-making and strategic planning for future projects. For instance, by analyzing commit patterns and pull request activity, managers can identify trends in team performance and areas for improvement. This data-driven approach allows for more informed resource allocation and project planning, ultimately leading to more successful outcomes.

Best Practices for Integrating Git Data with JIRA

To maximize the benefits of integrating Git data with JIRA, engineering managers should consider the following best practices:

Select the Right Tools

Choose integration tools that fit your team's specific needs. Tools like Typo can facilitate the connection between Git and JIRA smoothly. Additionally, JIRA integrates directly with several source control systems, allowing for automatic updates and real-time visibility.

‍

If you’re ready to enhance your project delivery speed and predictability, consider integrating Git data with your JIRA dashboards. Explore Typo! We can help you do this in a few clicks & make it one of your favorite dashboards.

Establish Commit Message Guidelines

Encourage your team to adopt consistent commit message guidelines. Including JIRA issue keys in commit messages will create a direct link between the code change and the JIRA issue. This practice not only enhances traceability but also aids in generating meaningful reports and insights. For example, a commit message like 'JIRA-123: Fixed the login issue' can help managers quickly identify relevant commits related to specific tasks.

Automate Workflows

Leverage automation features available in both JIRA and Git platforms to streamline the integration process. For instance, set up automated triggers that update JIRA issues based on events in Git, such as moving a JIRA issue to 'In Review' once a pull request is submitted in Git. This reduces manual updates and alleviates the administrative burden on the team.

Train Your Team

Providing adequate training to your team ensures everyone understands the integration process and how to effectively use both tools together. Conduct workshops or create user guides that outline the key benefits of integrating Git and JIRA, along with tips on how to leverage their combined functionalities for improved workflows.

Monitor and Adapt

Implement regular check-ins to assess the effectiveness of the integration. Gather feedback from team members on how well the integration is functioning and identify any pain points. This ongoing feedback loop allows you to make incremental improvements, ensuring the integration continues to meet the needs of the team.

Utilize Dashboards for Visualization

Create comprehensive dashboards that visually represent combined metrics from both Git and JIRA. Tools like JIRA dashboards, Confluence, or custom-built data visualization platforms can provide a clearer picture of project health. Metrics can include the number of active pull requests, average time in code review, or commit activity relevant to JIRA task completion.

Encourage Regular Code Reviews

With the changes being reflected in JIRA, create a culture around regular code reviews linked to specific JIRA tasks. This practice encourages collaboration among team members, ensures code quality, and keeps everyone aligned with project objectives. Regular code reviews also lead to knowledge sharing, which strengthens the team's overall skill set.

Case Study:

25% Improvement in Task Completion with Jira-Git Integration at Trackso

To illustrate the benefits of integrating Git data with JIRA, let’s consider a case study of a software development team at a company called Trackso.

Background

Trackso, a remote monitoring platform for Solar energy, was developing a new SaaS platform that consisted of a diverse team of developers, designers, and project managers. The team relied heavily on JIRA for tracking project statuses, but they found their productivity hampered by several issues:

• Tasks had vague statuses that did not reflect actual progress to project managers.
• Developers frequently worked in isolation without insight into each other's code contributions.
• They could not correlate project delays with specific code changes or reviews, leading to poor risk management.

Implementation of Git and JIRA Integration

In 2022, Trackso's engineering manager decided to integrate Git data with JIRA. They chose GitHub for version control, given its robust collaborative features. The team set up automatic links between their JIRA tickets and corresponding GitHub pull requests and standardized their commit messages to include JIRA issue keys.

Metrics of Improvement

After implementing the integration, Trackso experienced significant improvements within three months:

• Increased Collaboration: There was a 40% increase in code review participation as developers began referencing JIRA issues in their commits, facilitating clearer discussions during code reviews.

• Reduced Delivery Times: Average task completion times decreased by 25%, as developers could see almost immediately when tasks were being actively worked on or if blockers arose.

• Improved Risk Management: The team reduced project delays by 30% due to enhanced visibility. For example, the integration helped identify that a critical feature was lagging due to slow pull request reviews. This enabled team leads to improve their code review workflows.

• Boosted Developer Morale: Developer satisfaction surveys indicated that 85% of team member felt more engaged in their work due to improved communication and clarity around task statuses.

Challenges Faced

Despite these successes, Trackso faced challenges during the integration process:

• Initial Resistance: Some team member were hesitant to adopt new practices & new personal dashboard. The engineering manager organized training sessions to showcase the benefits of integrating Git and JIRA & having a personal dashboard, promoting buy-in from the team and leaving the default dashboard.

• Maintaining Commit Message Standards: Initially, not all developers consistently used the issue keys in their commit messages. The team revisited training sessions and created a shared repository of best practices to ensure adherence.

Conclusion

While JIRA dashboards are valuable tools for project management, they are insufficient on their own for engineering managers seeking to improve project delivery speed and predictability. By integrating Git data with JIRA, teams can gain richer insights into development activity, enhance collaboration, and manage risks more effectively. This holistic approach empowers engineering leaders to make informed decisions and drive continuous improvement in their software development processes. Embracing this integration will ultimately lead to better project outcomes and a more productive engineering culture. As the software development landscape continues to evolve, leveraging the power of both JIRA and Git data will be essential for teams looking to stay competitive and deliver high-quality products efficiently.

‍

In an ever-changing tech landscape, organizations need to stay agile and deliver high-quality software rapidly. DevOps plays a crucial role in achieving these goals by bridging the gap between development and operations teams. 

In this blog, we will delve into how to build a DevOps culture within your organization and explore the fundamental practices and strategies that can lead to more efficient, reliable, and customer-focused software development.

What is DevOps? 

DevOps is a software development methodology that integrates development (Dev) and IT operations (Ops) to enhance software delivery’s speed, efficiency, and quality. The primary goal is to break down traditional silos between development and operations teams and foster a culture of collaboration and communication throughout the software development lifecycle.  This creates a more efficient and agile workflow that allows organizations to respond quickly to changes and deliver value to customers faster.

Why DevOps Culture is Beneficial? 

DevOps culture refers to a collaborative and integrated approach between development and operations teams. It focuses on breaking down silos, fostering a shared sense of responsibility, and improving processes through automation and continuous feedback.

• Fostering collaboration between development and operations allows organizations to innovate more rapidly, and respond to market changes and customer needs effectively. 

• Automation and streamlined processes reduce manual tasks and errors to increase efficiency in software delivery. This efficiency results in faster time-to-market for new features and updates.

• Continuous integration and delivery practices improve software quality by early detection of issues. This helps maintain system stability and reliability.

• A DevOps culture encourages teamwork and mutual trust to improve collaboration between previously siloed teams. This cohesive environment fosters innovation and collective problem-solving. 

• DevOps culture results in faster recovery time as they can identify and address issues more swiftly, reducing downtime and improving overall service reliability.

• Delivering high-quality software quickly and efficiently enhances customer satisfaction and loyalty, which is vital for long-term success. 

The CALMS Framework of DevOps 

The CALMS framework is used to understand and implement DevOps principles effectively. It breaks down DevOps into five key components:

Culture

The culture pillar focuses on fostering a collaborative environment where shared responsibility and open communication are prioritized. It is crucial to break down silos between development and operations teams and allow them to work together more effectively. 

Automation

Automation emphasizes minimizing manual intervention in processes. This includes automating testing, deployment, and infrastructure management to enhance efficiency and reliability.

Lean

The lean aspect aims to optimize workflows, manage work-in-progress (WIP), and eliminate non-value-adding activities. This is to streamline processes to accelerate software delivery and improve overall quality.

Measurement

Measurement involves collecting data to assess the effectiveness of software delivery processes and practices. It enables teams to make informed, fact-based decisions, identify areas for improvement, and track progress. 

Sharing

The sharing component promotes open communication and knowledge transfer among teams It facilitates cross-team collaboration, fosters a learning environment, and ensures that successful practices and insights are shared and adopted widely.

Tips to Build a DevOps Culture

Start Simple 

Don’t overwhelm teams completely with the DevOps haul. Begin small and implement DevOps practice gradually. You can start first with the team that is better aligned with DevOps principles and then move ahead with other teams in the organization. Build momentum with early wins and evolve practices as you gain experience.

Foster Communication and Collaborative Environment 

Communication is a key. When done correctly, it promotes collaboration and a smooth flow of information across the organization. This further aligns organization operations and lets the engineering leaders make informed decisions. 

Moreover, the combined working environment between the development and operations teams promotes a culture of shared responsibility and common objectives. They can openly communicate ideas and challenges, allowing them to have a mutual conversation about resources, schedules, required features, and execution of projects. 

Create Common Goal 

Apart from encouraging communication and a collaborative environment, create a clear plan that outlines where you want to go and how you will get there. Ensure that these goals are realistic and achievable. This will allow teams to see the bigger picture and understand the desired outcome, motivating them to move in the right direction.

Focus on Automation 

Tools such as Slack, Kubernetes, Docker, and Jfrog help build automation capabilities for DevOps teams. These tools are useful as they automate repetitive and mundane tasks and allow teams to focus on value-adding work. This allows them to fail fast, build fast, and deliver quickly which enhances their efficiency and process acceleration, positively impacting DevOps culture. Ensure that instead of assuming, ask your team directly what part can be automated and further support facilities to automate it. 

Implement CI/CD pipeline

The organization must fully understand and implement CI/CD to establish a DevOps culture and streamline the software delivery process. This allows for automating deployment from development to production and releasing the software more frequently with better quality and reduced risks. The CI/CD tools further allow teams to catch bugs early in the development cycle, reduce manual work, and minimize downtime between releases. 

Foster Continuous Learning and Improvement

Continuous improvement is a key principle of DevOps culture. Engineering leaders must look for ways to encourage continuous learning and improvement such as by training and providing upskilling opportunities. Besides this, give them the freedom to experiment with new tools and techniques. Create a culture where they feel comfortable making mistakes and learning from them. 

Balance Speed and Security 

The teams must ensure that delivering products quickly doesn’t mean compromising security. In DevOps culture, the organization must adopt a ‘Security-first approach’ by integrating security practices into the DevOps pipeline. To maintain a strong security posture, regular security audits and compliance checks are essential. Security scans should be conducted at every stage of the development lifecycle to continuously monitor and assess security.

Monitor and Measure 

Regularly monitor and track system performance to detect issues early and ensure smooth operation. Use metrics and data to guide decisions, optimize processes, and continuously improve DevOps practices. Implement comprehensive dashboards and alerts to ensure teams can quickly respond to performance issues and maintain optimal health. 

Prioritize Customer Needs

In DevOps culture, the organization must emphasize the ever-evolving needs of the customers. Encourage teams to think from the customer’s perspective and keep their needs and satisfaction at the forefront of the software delivery processes. Regularly incorporate customer feedback into the development cycle to ensure the product aligns with user expectations.

Typo - An Effective Platform to Promote DevOps Culture

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 DORA and other 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.

‍

‍

Conclusion 

Building a DevOps culture is essential for organizations to improve their software delivery capabilities and maintain a competitive edge. Implementing key practices as mentioned above will pave the way for a successful DevOps transformation. 

‍

As platform engineering continues to evolve, it brings both promising opportunities and potential challenges. 

As we look to the future, what changes lie ahead for Platform Engineering? In this blog, we will explore the future landscape of platform engineering and strategize how organizations can stay at the forefront of innovation.

What is Platform Engineering? 

Platform Engineering is an emerging technology approach that enables software developers with all the required resources. It acts as a bridge between development and infrastructure which helps in simplifying the complex tasks and enhancing development velocity. The primary goal is to improve developer experience, operational efficiency, and the overall speed of software delivery.

Importance of Platform Engineering

• Platform engineering helps in creating reusable components and standardized processes. It also automates routine tasks, such as deployment, monitoring, and scaling, to speed up the development cycle.

• Platform engineering integrates security measures into the platform to ensure that applications are built and deployed securely. This allows the platform to meet regulatory and compliance requirements.

• It ensures efficient use of resources to balance performance and expenditure. It also provides transparency into resource usage and associated costs to help organizations make informed decisions about scaling and investment.

• By providing tools, frameworks, and services, platform engineering tool empowers developers to build, deploy, and manage applications more effectively.

• A well-engineered platform allows organizations to adapt quickly to market changes, new technologies, and customer needs.

Key Predictions for Platform Engineering

More Focus on Developer Experience

The rise in Platform Engineering will enhance developer experience by creating standard toolchains and workflow. In the coming time, the platform engineering team will work closely with developers to understand what they need to be productive. Moreover, the platform tool will be integrated and closely monitored through DevEx and reports. This will enable developers to work efficiently and focus on the core tasks by automating repetitive tasks, further improving their productivity and satisfaction. 

Rise in Internal Developer Platform 

Platform engineering is closely associated with the development of IDP. In today’s times, organizations are striving for efficiency, hence, the creation and adoption of internal development platforms will rise. This will streamline operations, provide a standardized way of deploying and managing applications, and reduce cognitive load. Hence, reducing time to market for new features and products, allowing developers to focus on delivering high-quality products more efficiently rather than managing infrastructure. 

Growing Trend of Ephemeral Environment 

Modern software development demands rapid iteration. The ephemeral environments, temporary, ideal environments, will be an effective way to test new features and bugs before they are merged into the main codebase. These environments will prioritize speed, flexibility, and cost efficiency. Since they are created on-demand and short-lived, they will align perfectly with modern development practices. 

Integration with Generative AI 

As times are changing, AI-driven tools become more prevalent. These Generative AI tools such as GitHub Copilot and Google Gemini will enhance capabilities such as infrastructure as code, governance as code, and security as code. This will not only automate manual tasks but also support smoother operations and improved documentation processes. Hence, driving innovation and automating dev workflow. 

Extension to DevOps 

Platform engineering is a natural extension of DevOps. In the future, the platform engineers will work alongside DevOps rather than replacing it to address its complexities and scalability challenges. This will provide a standardized and automated approach to software development and deployment leading to faster project initialization, reduced lead time, and increased productivity. 

Shift to Product-Centric Funding Model 

Software organizations are now shifting from project project-centric model towards product product-centric funding model. When platforms are fully-fledged products, they serve internal customers and require a thoughtful and user-centric approach in their ongoing development. It also aligns well with the product lifecycle that is ongoing and continuous which enhances innovation and reduces operational friction. It will also decentralize decision making which allows platform engineering leaders to make and adjust funding decisions for their teams. 

Why Staying Updated on Platform Engineering Trends is Crucial?

• Platform Engineering is a relatively new and evolving field. Hence, platform engineering teams need to keep up with rapid tech changes and ensure the platform remains robust and efficient.

• Emerging technologies such as serverless computers and edge computers will shape the future of platform engineering. Moreover, Artificial intelligence and machine learning also help in optimizing various aspects of software development such as testing and monitoring. 

• Platform engineering trends are introducing new ways to automate processes, manage infrastructure, and optimize workflows. This enables organizations to streamline operations, reduce manual work, and focus on more strategic tasks, leading to enhanced developer productivity. 

• A platform aims to deliver a superior user experience. When platform engineers stay ahead of the learning curve, they can implement features and improvements that improve the end-user experience, resulting in higher customer satisfaction and retention.

• Trends in platform engineering highlight new methods for building scalable and flexible systems. It allows platform engineers to design platforms that can easily adapt to changing demands and scale without compromising performance.

Typo - An Effective Platform Engineering Tool 

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.

Conclusion 

The future of platform engineering is both exciting and dynamic. As this field continues to evolve, staying ahead of these developments is crucial for organizations aiming to maintain a competitive edge. By embracing these predictions and proactively adapting to changes, platform engineering teams can drive innovation, improve efficiency, and deliver high-quality products that meet the demands of an ever-changing tech landscape.

‍

Platform engineering is a relatively new and evolving field in the tech industry. However, like any evolving field, it comes with its share of challenges. If overlooked can limit its effectiveness.

In this blog post, we dive deep into these common missteps and provide actionable insights to overcome them, so that your platform engineering efforts are both successful and sustainable.

What is Platform Engineering?

Platform Engineering refers to providing foundational tools and services to the development team that allow them to quickly and safely deliver their applications. This aims to increase developer productivity by providing a unified technical platform to streamline the process which helps reduce errors and enhance reliability. 

Core Components of Platform Engineering 

Internal Developer Platform (IDPs) 

The core component of Platform Engineering is IDP i.e. centralized collections of tools, services, and automated workflows that enable developers to self-serve resources needed for building, testing, and deploying applications. It empowers developers to deliver faster by reducing reliance on other teams, automating repetitive tasks, reducing the risk of errors, and ensuring every application adheres to organizational standards.

Platform Team 

The platform team consists of platform engineers who are responsible for building, maintaining, and configuring the IDP. The platform team standardizes workflows, automates repetitive tasks, and ensures that developers have access to the necessary tools and resources. The aim is to create a seamless experience for developers. Hence, allowing them to focus on building applications rather than managing infrastructure. 

Automation and Standardization

Platform engineering focuses on the importance of standardizing processes and automating infrastructure management. This includes creating paved roads for common development tasks such as deployment scripts, testing, and scaling to simplify workflows and reduce friction for developers. Curating a catalog of resources, following predefined templates, and establishing best practices ensure that every deployment follows the same standards, thus enhancing consistency across development efforts while allowing flexibility for individual preferences. 

Continuous Improvement 

Platform engineering is an iterative process, requiring ongoing assessment and enhancement based on developer feedback and changing business needs. This results in continuous improvement that ensures the platform evolves to meet the demands of its users and incorporates new technologies and practices as they emerge. 

Security and Compliance

Security is a key component of platform engineering. Integrating security best practices into the platform such as automated vulnerability scanning, encryption, and compliance monitoring is the best way to protect against vulnerabilities and ensure compliance with relevant regulations. This proactive approach is integrated into all stages of the platform helps mitigate risks associated with software delivery and fosters a secure development environment. 

Common Mistakes in Platform Engineering

Focusing Solely on Dashboards

One of the common mistakes platform engineers make is focusing solely on dashboards without addressing the underlying issues that need solving. While dashboards provide a good overview, they can lead to a superficial understanding of problems instead of encouraging genuine process improvements. 

To avoid this, teams must combine dashboards with automated alerts, tracing, and log analysis to get actionable insights and a more comprehensive observability strategy for faster incident detection and resolution. 

Building without Understanding the Developers’ Needs

Developing a platform based on assumptions ends up not addressing real problems and does not meet the developers’s needs. The platform may lack important features for developers leading to dissatisfaction and low adoption. 

Hence, establishing clear objectives and success criteria vital for guiding development efforts. Engage with developers now and then. Conduct surveys, interviews, or workshops to gather insights into their pain points and needs before building the platform.

Overengineering the Platform 

Building an overlay complex platform hinders rather than helps development efforts. When the platform contains features that aren’t necessary or used by developers, it leads to increased maintenance costs and confusion among developers that further hampers their productivity. 

The goal must be finding the right balance between functionality and simplicity. Hence, ensuring the platform effectively meets the needs of developers without unnecessary complications and iterating it based on actual usage and feedback.

Encouraging One-Size-Fits-All Solution

The belief that a single platform caters to all development teams and uses cases uniformly is a fallacy. Different teams and applications have varying needs, workflows, and technology stacks, necessitating tailored solutions rather than a uniform approach. As a result, the platform may end up being too rigid for some teams and overly complex for some resulting in low adoption and inefficiencies. 

Hence, design a flexible and customizable platform that adapts to diverse requirements. This allows teams to tailor the platform to their specific workflows while maintaining shared standards and governance.

Overplanning and under-executing

Spending excessive time in the planning phase leads to delays in implementation, missed opportunities, and not fully meeting the evolving needs of end-users. When the teams focus on perfecting every detail before implementation it results in the platform remaining theoretical instead of delivering real value.

An effective way is to create a balance between planning and executing by adopting an iterative approach. In other words, focus on delivering a minimum viable product (MVP) quickly and continuously improving it based on real user feedback. This allows the platform to evolve in alignment with actual developer needs which ensures better adoption and more effective outcomes.

Failing to Prioritize Security

Building the platform without incorporating security measures from the beginning can create opportunities for cyber threats and attacks. This also exposes the organization to compliance risks, vulnerabilities, and potential breaches that could be costly to resolve.

Implementing automated security tools, such as identity and access management (IAM), encrypted communications, and code analysis tools helps continuously monitor for security issues and ensure compliance with best practices. Besides this, provide ongoing security training that covers common vulnerabilities, secure coding practices, and awareness of evolving threats.

Benefits of Platform Engineering 

When used correctly, platform engineering offers many benefits: 

• Platform engineering improves developer experience by offering self-service capabilities and standardized tools. It allows the team to focus on building features and deliver products more efficiently and effectively.

• It increases the reliability and security of applications by providing a stable foundation and centralized infrastructure management.

• Engineering teams can deploy applications and updates faster with a robust and automated platform that accelerates the time-to-market for new features and products.

• Focusing on scalable solutions allows Platform engineering to enable the underlying systems to handle increased demand without compromising performance and grow their applications and services efficiently.

• A solid platform foundation allows teams to experiment with new technologies and methodologies. Hence, supporting innovation and the adoption of modern practices.

Typo - An Effective Platform Engineering Tool 

Typo is an effective platform engineering tool 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.

‍

‍

Conclusion

Platform engineering has immense potential to streamline development and improve efficiency, but avoiding common pitfalls is key. By focusing on the pitfalls mentioned above, you can create a platform that drives productivity and innovation. 

‍

All the best! :) 

‍

‍What is Clean Code? 

Robert C. Martin introduced the ‘Clean Code’ concept in his book ‘Clean Code: A Handbook of Agile Software Craftsmanship’. He defined clean code as: 

“A code that has been taken care of. Someone has taken the time to keep it simple and orderly. They have laid appropriate attention to details. They have cared.”

Clean code is easy to read, understand, and maintain. It is well structured and free of unnecessary complexity, code smell, and anti-patterns. 

Key Characteristics that Define Clean Code

• The code is easy to read and understand. The names are descriptive of variables, functions, and classes, and the code is structured for a clear purpose. 

• The code is simple and doesn’t include any unnecessary complexity. 

• The code is consistent in naming conventions, formatting, and organization to help maintain readability. 

• The code is easy to test and free from bugs and errors. 

• The code is easy to update and modify. 

• Clean code is regularly refactored and free from redundancy. 

Clean Code Principles 

Single Responsibility Principle 

This principle states that each module or function should have a defined responsibility and one reason to change. Otherwise, it can result in bloated and hard-to-maintain code. 

Example: the code’s responsibilities are separated into three distinct classes: User, Authentication, and EmailService. This makes the code more modular, easier to test, and easier to maintain.

class User {

  constructor(name, email, password) {

    this.name = name;

    this.email = email;

    this.password = password;

  }

}

‍

class Authentication {

  login(user, password) {

    // ... login logic

  }

‍

  register(user, password) {

    // ... registration logic

  }

}

‍

class EmailService {

  sendVerificationEmail(email) {

    // ... email sending logic

  }

}

DRY Principle (Don’t Repeat Yourself) 

The DRY Principle states that unnecessary duplication and repetition of code must be avoided. If not followed, it can increase the risk of inconsistency and redundancy. Instead, you can abstract common functionality into reusable functions, classes, or modules.

‍

Example: The common greeting formatting logic is extracted into a reusable formatGreeting function, which makes the code DRY and easier to maintain.

‍

function formatGreeting(name, message) {

  return message + ", " + name + "!";

}

‍

function greetUser(name) {

  console.log(formatGreeting(name, "Hello"));

}

‍

function sayGoodbye(name) {

  console.log(formatGreeting(name, "Goodbye"));

}

YAGNI – you aren’t gonna need it

YAGNI is an extreme programming practice that states “Always implement things when you actually need them, never when you just foresee that you need them.” 

It doesn’t mean avoiding flexibility in code but rather not overengineer everything based on assumptions about future needs. The principle means delivering the most critical features on time and prioritizing them based on necessity. 

Kiss - Keep it Simple, Stupid 

This principle states that the code must be simple over complex to enhance comprehensibility, usability, and maintainability. Direct and clear code is better to avoid making it bloated or confusing. 

‍

Example: The function directly multiplies the length and width to calculate the area and there are no extra steps or conditions that might confuse or complicate the code.

‍

def calculate_area(length, width):

    return length * width

The Boy Scout Rule 

According to ‘The Boy Scout Rule’, always leave the code in a better state than you found it. In other words, make continuous, small enhancements whenever engaging with the codebase. It could be either adding a feature or fixing a bug. It encourages continuous improvement and maintains a high-quality codebase over time. 

‍

Example: The original code had unnecessary complexity due to the redundant variable and nested conditional. The cleaned-up code is more concise and easier to understand.

‍

Before: 

‍

def factorial(n):

    if n == 0:

        return 1

    else:

        return n * factorial(n - 1)

‍

# Before:

result = factorial(5)

print(result)

‍

# After:

print(factorial(5))

‍

After: 

‍

def factorial(n):

    return 1 if n == 0 else n * factorial(n - 1)

Fail Fast

This principle indicates that the code must fail as early as possible. This limits the bugs that make it into production and promptly addresses errors. This ensures the code remains clean, reliable, and usable. 

Open/Closed Principle 

As per the Open/Closed Principle, the software entities should be open to extension but closed to modification. This means that team members must add new functionalities to an existing software system without changing the existing code. 

‍

Example: The Open/Closed Principle allows adding new employee types (like "intern" or "contractor") without modifying the existing calculate_salary function. This makes the system more flexible and maintainable.

‍

Without the Open/Closed Principle 

‍

def calculate_salary(employee_type):

    if employee_type == "regular":

        return base_salary

    elif employee_type == "manager":

        return base_salary * 1.5

    elif employee_type == "executive":

        return base_salary * 2

    else:

        raise ValueError("Invalid employee type")

‍

With the Open/Closed Principle 

‍

class Employee:

    def calculate_salary(self):

        raise NotImplementedError()

‍

class RegularEmployee(Employee):

    def calculate_salary(self):

        return base_salary

‍

class Manager(Employee):

    def calculate_salary(self):

        return base_salary * 1.5

‍

class Executive(Employee):

    def calculate_salary(self):

        return base_salary * 2

Practice Consistently 

When you choose to approach something in a specific way, ensure maintaining consistency throughout the entire project. This includes consistent naming conventions, coding styles, and formatting. It also ensures that the code aligns with team standards, to make it easier for others to understand and work with. Consistent practice also allows you to identify areas for improvement and learn new techniques.

Favor composition over inheritance

This means to use ‘has-a’ relationships (containing instances of other classes) instead of ‘is-a’ relationships (inheriting from a superclass). This makes the code more flexible and maintainable.

‍

Example: In this example, the SportsCar class has a Car object as a member, and it can also have additional components like a spoiler. This makes it more flexible, as we can easily create different types of cars with different combinations of components.

‍

class Engine:

    def start(self):

        pass

‍

class Car:

    def __init__(self, engine):

        self.engine = engine

‍

class SportsCar(Car):

    def __init__(self, engine, spoiler):

        super().__init__(engine)

        self.spoiler = spoiler

Avoid Hard-Coded Number

Avoid hardcoded numbers, rather use named constants or variables to make the code more readable and maintainable.

‍

Example: 

‍

Instead of: 

discount_rate = 0.2

‍

Use: 

DISCOUNT_RATE = 0.2

‍

This makes the code more readable and easier to modify if the discount rate needs to be changed.

Typo - An Automated Code Review Tool

Typo’s automated code review tool enables developers to catch issues related to code issues and detect code smells and potential bugs promptly. 

‍

With automated code reviews, auto-generated fixes, and highlighted hotspots, Typo streamlines the process of merging clean, secure, and high-quality code. It automatically scans your codebase and pull requests for issues, generating safe fixes before merging to master. Hence, ensuring your code stays efficient and error-free.

‍

‍

The ‘Goals’ feature empowers engineering leaders to set specific objectives for their tech teams that directly support writing clean code. By tracking progress and providing performance insights, Typo helps align teams with best practices, making it easier to maintain clean, efficient code. The goals are fully customizable, allowing you to set tailored objectives for different teams simultaneously.

‍

Conclusion 

Writing clean code isn’t just a crucial skill for developers. It is an important way to sustain software development projects.

By following the above-mentioned principles, you can develop a habit of writing clean code. It will take time but it will be worth it in the end.

‍

Platform engineering is a relatively new and evolving field in the tech industry. To make the most of Platform Engineering, there are several best practices you should be aware of.

In this blog, we explore these practices in detail and provide insights into how you can effectively implement them to optimize your development processes and foster innovation.

What is Platform Engineering?

Platform Engineering, an emerging technology approach, is the practice of designing and managing the infrastructure and tools that support software development and deployment. This is to help them perform end-to-end operations of software development lifecycle automation. The aim is to reduce overall cognitive load, increase operational efficiency, and remove process bottlenecks by providing a reliable and scalable platform for building, deploying, and managing applications. 

Importance of Platform Engineering

• Platform engineering improves developer experience by offering self-service capabilities and standardized tools. It allows the team to focus on building features and deliver products more efficiently and effectively. 

• It increases the reliability and security of applications by providing a stable foundation and centralized infrastructure management.

• Engineering teams can deploy applications and updates faster with a robust and automated platform that accelerates the time-to-market for new features and products.

• Focusing on scalable solutions allows Platform engineering to enable the underlying systems to handle increased demand without compromising performance and grow their applications and services efficiently. 

• A solid platform foundation allows teams to experiment with new technologies and methodologies. Hence, supporting innovation and the adoption of modern practices.

Platform Engineering Best Practices

The platform Must be Developer-Centric

Always treat your platform engineering team as paying customers. This allows you to understand developers’ pain points, preferences, and requirements and focus on making the development process easier and more efficient. Some of the key points that are taken into consideration:

• User-friendly tools to streamline the workflow. 

• Must feel at ease while navigating the platform. 

• Seamlessly integrates with existing and other third-party applications. 

• Allow them to access and manage resources without needing extensive support.

When the above-mentioned needs and requirements are met, end-users are likely to adopt this platform enthusiastically. Hence, making the platform more effective and productive. 

Adopt Security Best Practices

Implement security control at every layer of the platform. Make sure that audit security posture is conducted regularly and that everyone on the team is updated with the latest security patches. Besides this, conduct code reviews and code analysis to identify and fix security vulnerabilities quickly. Educate your platform engineering team about security practices and offer them ongoing training and mentorship so they are constantly upskilling. 

Foster Continuous Improvement and Feedback Loops

Continuous improvement must be a core principle to allow the platform to evolve according to technical trends. Integrate feedback mechanisms with the internal developer platform to gather insights from the software development lifecycle. Regularly review and improve the platform based on feedback from development teams. This enables rapid responses to any impediments developers face. 

Encourage a Culture of Collaboration

Foster communication and knowledge sharing among platform engineers. Align them with common goals and objects and recognize their collaborative efforts. This helps teams to understand how their work contributes to the overall success of the platform which further, fosters a sense of unity and purpose. It also ensures that all stakeholders understand how to effectively use the platform and contribute to its continuous improvement. 

Platform Team must have a Product Mindset

View your internal platform as a product that requires management and ongoing development. The platform team must be driven by a product mindset that includes publishing roadmaps, gathering user feedback, and fostering a customer-centric approach. They must focus on what offers real value to their internal customers and app developers based on the feedback, so it addresses the pain points quickly. 

Maintain DevOps Culture

Emphasize the importance of a DevOps culture that prioritizes collaboration between development and operations teams that focuses on learning and improvement rather than assigning time. It is crucial to foster an environment where platform engineering can thrive and foster a shared responsibility for the software lifecycle.

Typo - An Effective Platform Engineering Tool 

Typo is an effective platform engineering tool 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.

‍

Conclusion

Platform Engineering is reshaping how we approach software development by streamlining infrastructure management and improving operational efficiency. Adhering to best practices allows organizations to harness the full potential of their platforms. Embracing these principles will optimize your development processes, drive innovation, and ensure a stable foundation for future growth.

‍

The era when development and operations teams worked in isolation, rarely interacting, is over. This outdated approach led to significant delays in developing and launching new applications. Modern IT leaders understand that DevOps is a more effective strategy.

DevOps fosters collaboration between software development and IT operations, enhancing the speed, efficiency, and quality of software delivery. By leveraging DevOps tools, the software development process becomes more streamlined through improved team collaboration and automation.

Understanding DevOps

DevOps is a methodology that merges software development (Dev) with IT operations (Ops) to shorten the development lifecycle while maintaining high software quality.

Creating a DevOps culture promotes collaboration, which is essential for continuous delivery. IT operations and development teams share ideas and provide prompt feedback, accelerating the application launch cycle.

Importance of DevOps for Startups

In the competitive startup environment, time equates to money. Delayed product launches risk competitors beating you to market. Even with an early market entry, inefficient development processes can hinder timely feature rollouts that customers need.

Implementing DevOps practice helps startups keep pace with industry leaders, speeding up development without additional resource expenditure, improving customer experience, and aligning with business needs.

Core Principles of DevOps

The foundation of DevOps rests on the principles of culture, automation, measurement, and sharing (CAMS). These principles drive continuous improvement and innovation in startups.

Key Benefits of DevOps for Startups

Faster Time-to-Market

DevOps accelerates development and release processes through automated workflows and continuous feedback integration.

• Startups can rapidly launch new features, fix bugs, and update software, gaining a competitive advantage.

• Implement continuous integration and continuous deployment (CI/CD) pipelines.

• Use automated testing to identify issues early.

Improved Efficiency

DevOps enhances workflow efficiency by automating repetitive tasks and minimizing manual errors.

• Utilize configuration management tools like Ansible and Chef.

• Implement containerization with Docker for consistency across environments.

• Jenkins for CI/CD

• Docker for containerization

• Kubernetes for orchestration

Enhanced Reliability

DevOps ensures code changes are continuously tested and validated, reducing failure risks.

• Conduct regular automated testing.

• Continuously monitor applications and infrastructure.

• Increased reliability leads to higher customer satisfaction and retention.

DevOps Practices for Startups

Embrace Automation with CI/CD Tools

Automation tools are essential for accelerating the software delivery process. Startups should use CI/CD tools to automate testing, integration, and deployment. Recommended tools include:

• Jenkins: An open-source automation server that supports building and deploying applications.

• GitLab CI/CD: Integrated CI/CD capabilities within GitLab for seamless pipeline management.

• CircleCI: A cloud-based CI/CD tool that offers fast builds and easy integration with various services.

Implement Continuous Integration and Continuous Delivery (CI/CD)

CI/CD practices enable frequent code changes and deployments. Key components include:

• Version Control Systems (VCS): Use Git with platforms like GitHub or Bitbucket for efficient code management.

• Build Automation: Tools like Maven or Gradle for Java projects, or npm scripts for Node.js, automate the build process.

• Deployment Automation: Utilize tools like Spinnaker or Argo CD for managing Kubernetes deployments.

Utilize Infrastructure as Code (IaC)

IaC allows startups to manage infrastructure through code, ensuring consistency and reducing manual errors. Consider using:

• Terraform: For provisioning and managing cloud infrastructure in a declarative manner.

• AWS CloudFormation: For defining infrastructure using YAML or JSON templates.

• Ansible: For configuration management and application deployment.

Adopt Containerization

Containerization simplifies deployment and improves resource utilization. Use:

• Docker: To package applications and their dependencies into lightweight, portable containers.

• Kubernetes: For orchestrating containerized applications, enabling scaling and management.

Monitor and Measure Performance

Implement robust monitoring tools to gain visibility into application performance. Recommended tools include:

• Prometheus: For real-time monitoring and alerting.

• Grafana: For visualizing metrics and logs.

• ELK Stack (Elasticsearch, Logstash, Kibana): For centralized logging and data analysis.

Integrate Security (DevSecOps)

Incorporate security practices into the DevOps pipeline using:

• Snyk: For identifying vulnerabilities in open-source dependencies.

• SonarQube: For continuous inspection of code quality and security vulnerabilities.

• HashiCorp Vault: For managing secrets and protecting sensitive data.

Leverage Software Engineering Intelligence (SEI) Platforms

SEI platforms provide critical insights into the engineering processes, enhancing decision-making and efficiency. Key features include:

• Data Integration: SEI platforms like Typo ingest data from various tools (e.g., GitHub, JIRA) to provide a holistic view of the development pipeline.

• Actionable Insights: These platforms analyze data to identify bottlenecks and inefficiencies, enabling teams to optimize workflows and improve delivery speed.

• DORA Metrics: SEI platforms track key metrics such as deployment frequency, lead time for changes, change failure rate, and time to restore service, helping teams measure their performance against industry standards.

Foster Collaboration and Communication

Utilize collaborative tools to enhance communication among team members. Recommended tools include:

• Slack: For real-time communication and integration with other DevOps tools.

• JIRA: For issue tracking and agile project management.

• Confluence: For documentation and knowledge sharing.

Encourage Continuous Learning

Promote a culture of continuous learning through:

• Internal Workshops: Regularly scheduled sessions on new tools or methodologies.

• Online Courses: Encourage team members to take courses on platforms like Coursera or Udemy.

Establish Clear Standards and Documentation

Create a repository for documentation and coding standards using:

• Markdown: For easy-to-read documentation within code repositories.

• GitHub Pages: For hosting project documentation directly from your GitHub repository.

How Typo Helps DevOps Teams?

Typo is a powerful tool designed specifically for tracking and analyzing DevOps metrics. It provides an efficient solution for dev and ops teams seeking precision in their performance measurement.

‍

‍

• With pre-built integrations in the dev tool stack, the dashboard provides all the relevant data within minutes.

• It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.

• The dashboard sets custom improvement goals for each team and tracks their success in real time.

• It gives real-time visibility into a team’s KPI and lets them make informed decisions.

Conclusion

Implementing DevOps best practices can markedly boost the agility, productivity, and dependability of startups.

By integrating continuous integration and deployment, leveraging infrastructure as code, employing automated testing, and maintaining continuous monitoring, startups can effectively tackle issues like limited resources and skill shortages.

Moreover, fostering a cooperative culture is essential for successful DevOps adoption. By adopting these strategies, startups can create durable, scalable solutions for end users and secure long-term success in a competitive landscape.

‍

DORA metrics offer a valuable framework for assessing software delivery performance throughout the software delivery lifecycle. Measuring DORA key metrics allows engineering leaders to identify bottlenecks, improve efficiency, and enhance software quality, which impacts customer satisfaction. It is also a key indicator for measuring the effectiveness of continuous delivery pipelines.

In this blog post, we delve into the pros and cons of utilizing DORA metrics to optimize continuous delivery processes, exploring their impact on performance, efficiency, and delivering high-quality software

What are DORA Metrics?

DORA metrics were developed by the DORA team founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren. These metrics are key performance indicators that measure the effectiveness and efficiency of the software delivery process and provide a data-driven approach to evaluate the impact of operational practices on software delivery performance.

Four Key DORA Metrics

• Deployment Frequency measures how often code is deployed into production per week.

• Lead Time for Changes measures the time it takes for code changes to move from inception to deployment.

• Change Failure Rate measures the code quality released to production during software deployments.

• Mean Time to Recover measures the time to recover a system or service after an incident or failure in production.

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.

Importance of Continuous Delivery for DORA Metrics

Continuous delivery (CD) is a primary aspect of modern software development that automatically prepares code changes for release to a production environment. It is combined with continuous integration (CI) and together, these two practices are known as CI/CD.

CD pipelines hold significant importance compared to traditional waterfall-style development. A few of them are:

Faster Time to Market

Continuous Delivery allows more frequent releases, allowing new features, improvements, and bug fixes to be delivered to end-users more quickly. It provides a competitive advantage by keeping the product up-to-date and responsive to user needs, which enhances customer satisfaction.

Improved Quality and Reliability

Automated testing and consistent deployment processes catch bugs and issues early. It improves the overall quality and reliability of the software and reduces the chances of defects reaching production.

Reduced Deployment Risk

When updates are smaller and more frequent, it reduces the complexity and risk associated with each deployment. If an issue does arise, it becomes easier to pinpoint the problem and roll back the changes.

Scalability

CD practices can be scaled to accommodate growing development teams and more complex applications. It helps to manage the increasing demands of modern software development.

Innovation and Experimentation

Continuous delivery allows teams to experiment with new ideas and features efficiently. This encourages innovation by allowing quick feedback and iteration cycles. 

Enhances Performance Visibility

• Deployment Frequency: High deployment frequency indicates a team’s ability to deliver updates and new features quickly and consistently.

• Lead Time for Changes: Short lead times suggest a more efficient delivery process.

• Change Failure Rate: A lower rate highlights better testing and higher quality in releases.

• Mean Time to Restore (MTTR): A lower MTTR indicates a team’s capability to respond to and fix issues rapidly.

Increases Operational Efficiency

Implementing DORA metrics encourages teams to streamline their processes, reducing bottlenecks and inefficiencies in the delivery pipeline. It also allows the team to regularly measure and analyze these metrics which fosters a culture of continuous improvement. As a result, teams are motivated to identify and resolve inefficiencies.

Fosters Collaboration and Communication

Tracking DORA metrics encourages collaboration between DevOps and other stakeholders. Hence, fostering a more integrated and cooperative approach to software delivery. It further provides objective data that teams can use to make informed decisions, prioritize work, and align their efforts with business goals.

Improves Software Quality

Continuous Delivery relies heavily on automated testing to catch defects early. DORA metrics help software teams track the testing processes’ effectiveness which ensures higher software quality. Faster deployment cycles and lower lead times enable quicker feedback from end-users. It allows software development teams to address issues and improve the product more swiftly.

Increases Reliability and Stability

Software teams can ensure that their deployments are more reliable and less prone to issues by monitoring and aiming to reduce the change failure rate. A low MTTR demonstrates a team’s capability to quickly recover from failures which minimizes downtime and its impact on users. Hence, increases the reliability and stability of the software.

Effective Incident Management

Incident management is an integral part of CD as it helps quickly address and resolve any issues that arise. This aligns with the DORA metric for Time to Restore Service as it ensures that any disruptions are quickly addressed, minimizing downtime, and maintaining service reliability.

Cons of DORA Metrics for Continuous Delivery

Implementation Challenges

The process of setting up the necessary software to measure DORA metrics accurately can be complex and time-consuming. Besides this, inaccurate or incomplete data can lead to misleading metrics which can affect decision-making and process improvements.

Resource Allocation Issues

Implementing and maintaining the necessary infrastructure to track DORA metrics can be resource-intensive. It potentially diverts resources from other important areas and increases the risk of disproportionately allocating resources to high-performing teams or projects to improve metrics.

Limited Scope of Metrics

DORA metrics focus on specific aspects of the delivery process and may not capture other crucial factors including security, compliance, or user satisfaction. It is also not universally applicable as the relevance and effectiveness of DORA metrics can vary across different types of projects, teams, and organizations. What works well for one team may not be suitable for another.

Cultural Resistance

Implementing DORA DevOps metrics requires changes in culture and mindset, which can be met with resistance from teams that are accustomed to traditional methods. Apart from this, ensuring that DORA metrics align with broader business goals and are understood by all stakeholders can be challenging.

Subjectivity in Measurement

While DORA Metrics are quantitative in nature, their interpretation and application of DORA metrics can be highly subjective. The definition and measuring metrics like ‘Lead Time for Changes’ or ‘MTTR’ can vary significantly across teams. It may result in inconsistencies in how these metrics are understood and applied across different teams.

How does Typo Solve this Issue?

As the tech landscape is evolving, there is a need for diverse evaluation tools in software development. Relying solely on DORA metrics can result in a narrow understanding of performance and progress. Hence, software development organizations necessitate a multifaceted evaluation approach.

And that’s why, Typo is here at your rescue!

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. Its automated code tool helps identify issues in the code and auto-fixes them before you merge to master.‍

‍

Features

• Offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.

• Includes effective sprint analysis feature that tracks and analyzes the team’s progress throughout a sprint.

• Provides 360 views of the developer experience i.e. captures qualitative insights and provides an in-depth view of the real issues.

• Offers engineering benchmark to compare the team’s results across industries.

• User-friendly interface.‍

‍

‍

Conclusion

While DORA metrics offer valuable insights into software delivery performance, they have their limitations. Typo provides a robust platform that complements DORA metrics by offering deeper insights into developer productivity and workflow efficiency, helping engineering teams achieve the best possible software delivery outcomes.

Scrum is known to be a popular methodology for software development. It concentrates on continuous improvement, transparency, and adaptability to changing requirements. Scrum teams hold regular ceremonies, including Sprint Planning, Daily Stand-ups, Sprint Reviews, and Sprint Retrospectives, to keep the process on track and address any issues.

With the help of DORA DevOps Metrics, Scrum teams can gain valuable insights into their development and delivery processes.

In this blog post, we discuss how DORA metrics help boost scrum team performance. 

What are DORA Metrics? 

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 DORA metrics are: 

• Deployment Frequency: Deployment Frequency measures the rate of change in software development and highlights potential bottlenecks. It is a key indicator of agility and efficiency. High Deployment Frequency signifies a streamlined pipeline, allowing teams to deliver features and updates faster.

• Lead Time for Changes: Lead Time for Changes measures the time it takes for code changes to move from inception to deployment. It tracks the speed and efficiency of software delivery and offers valuable insights into the effectiveness of development processes, deployment pipelines, and release strategies.

• Change Failure Rate: Change Failure Rate measures the frequency of newly deployed changes leading to failures, glitches, or unexpected outcomes in the IT environment. It reflects reliability and efficiency and is related to team capacity, code complexity, and process efficiency, impacting speed and quality.

• Mean Time to Recover: Mean Time to Recover measures the average duration a system or application takes to recover from a failure or incident. It concentrates on determining the efficiency and effectiveness of an organization's incident response and resolution procedures.

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.

Why DORA Metrics are Useful for Scrum Team Performance? 

DORA metrics are useful for Scrum team performance because they provide key insights into the software development and delivery process. Hence, driving operational performance and improving developer experience.

Measure Key Performance Indicators (KPIs)

DORA metrics track crucial KPIs such as deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate which helps Scrum teams understand their efficiency and identify areas for improvement.

Enhance Workflow Efficiency

Teams can streamline their software delivery process and reduce bottlenecks by monitoring deployment frequency and lead time for changes. Hence, leading to faster delivery of features and bug fixes.

Improve Reliability 

Tracking the change failure rate and MTTR helps software teams focus on improving the reliability and stability of their applications. Hence, resulting in more stable releases and fewer disruptions for users.

Encourage Data-Driven Decision Making 

DORA metrics give clear data that helps teams decide where to improve, making it easier to prioritize the most impactful actions for better performance and enhanced customer satisfaction.

Foster Continuous Improvement

Regularly reviewing these metrics encourages a culture of continuous improvement. This helps software development teams to set goals, monitor progress, and adjust their practices based on concrete data.

Benchmarking

DORA metrics allow DevOps teams to compare their performance against industry standards or other teams within the organization. This encourages healthy competition and drives overall improvement.

Provide Actionable Insights

DORA metrics provide actionable data that helps Scrum teams identify inefficiencies and bottlenecks in their processes. Analyzing these metrics allows engineering leaders to make informed decisions about where to focus improvement efforts and reduce recovery time.

Best Practices for Implementing DORA Metrics in Scrum Teams

Understand the Metrics 

Firstly, understand the importance of DORA Metrics as each metric provides insight into different aspects of the development and delivery process. Together, these metrics offer a comprehensive view of the team’s performance and allow them to make data-driven decisions. 

Set Baselines and Goals

Scrum teams should start by setting baselines for each metric to get a clear starting point and set realistic goals. For instance, if a scrum team currently deploys once a month, it may be unrealistic to aim for multiple deployments per day right away. Instead, they could set a more achievable goal, like deploying once a week, and gradually work towards increasing their frequency.

Regularly Review and Analyze Metrics

Scrum teams must schedule regular reviews (e.g., during sprint retrospectives) to discuss the metrics to identify trends, patterns, and anomalies in the data. This helps to track progress, pinpoint areas for improvement, and further allow them to make data-driven decisions to optimize their processes and adjust their goals as needed.

Foster Continuous Growth

Use the insights gained from the metrics to drive ongoing improvements and foster a culture that values experimentation and learning from mistakes. By creating this environment, Scrum teams can steadily enhance their software delivery performance. Note that, this approach should go beyond just focusing on DORA metrics. it should also take into account other factors like developer productivity and well-being, collaboration, and customer satisfaction.

Ensure Cross-Functional Collaboration and Communicate Transparently

Encourage collaboration between development, operations, and other relevant teams to share insights and work together to address bottlenecks and improve processes. Make the metrics and their implications transparent to the entire team. You can use the DORA Metrics dashboard to keep everyone informed and engaged.

How Typo Leverages DORA Metrics? 

Typo is a powerful tool designed specifically for tracking and analyzing DORA metrics. It provides an efficient solution for DevOps and Scrum teams seeking precision in their performance measurement.

• With pre-built integrations in the dev tool stack, the DORA metrics dashboard provides all the relevant data within minutes.

• It helps in deep diving and correlating different metrics to identify real-time bottlenecks, sprint delays, blocked PRs, deployment efficiency, and much more from a single dashboard.

• The dashboard sets custom improvement goals for each team and tracks their success in real-time.

• It gives real-time visibility into a team’s KPI and allows real-time them to make informed decisions.

‍

‍

Conclusion 

Leveraging DORA Metrics can transform Scrum team performance by providing actionable insights into key aspects of development and delivery. When implemented the right way, teams can optimize their workflows, enhance reliability, and make informed decisions to build high-quality software.

‍

‍

Platform Engineering is becoming increasingly crucial. According to the 2024 State of DevOps Report: The Evolution of Platform Engineering, 43% of organizations have had platform teams for 3-5 years. The field offers numerous benefits, such as faster time-to-market, enhanced developer happiness, and the elimination of team silos.

However, there is one critical piece of advice that Platform Engineers often overlook: treat your platform as an internal product and consider your wider teams as your customers.

So, how can they do this effectively? It's important to measure what’s working and what isn’t using consistent indicators of success.

In this blog, we’ve curated the top platform engineering KPIs that software teams must monitor:

What is Platform Engineering?

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. 

Importance of Tracking Platform Engineering KPIs

Helps in Performance Monitoring and Optimization

Platform Engineering KPIs offer insights into how well the platform performs under various conditions. They also help to identify loopholes and areas that need optimization to ensure the platform runs efficiently.

Ensures Scalability and Capacity Planning

These metrics guide decisions on how to scale resources. It also ensures the capacity planning i.e. the platform can handle growth and increased load without performance degradation. 

Quality Assurance

Tracking KPIs ensure that the platform remains robust and maintainable. This further helps to reduce technical debt and improve the platform’s overall quality. 

Increases Productivity and Collaboration

They provide in-depth insights into how effectively the engineering team operates and help to identify areas for improvement in team dynamics and processes.

Fosters a Culture of Continuous Improvement

Regularly tracking and analyzing KPIs fosters a culture of continuous improvement. Hence, encouraging proactive problem-solving and innovation among platform engineers. 

Top Platform Engineering KPIs to Track 

Deployment Frequency 

Deployment Frequency measures how often code is deployed into production per week. It takes into account everything from bug fixes and capability improvements to new features. It is a key metric for understanding the agility and efficiency of development and operational processes and highlights the team’s ability to deliver updates and new features.

The higher frequency with minimal issues reflects mature CI/CD processes and how platform engineering teams can quickly adapt to changes. Regularly tracking and adapting Deployment Frequency helps in continuous improvement as it reduces the risk of large, disruptive changes and delivers value to end-users effectively. 

Lead Time for Changes

Lead Time is the duration between a code change being committed and its successful deployment to end-users. It is correlated with both the speed and quality of the platform engineering team. Higher lead time gives a clear sign of roadblocks in processes and the platform needs attention. 

Low lead time indicates that the teams quickly adapt to feedback and deliver products timely. It also gives teams the ability to make rapid changes, allowing them to adapt to evolving user needs and market conditions. Tracking it regularly helps in streamlining workflows and reducing bottlenecks. 

Change Failure Rate

Change Failure Rate refers to the proportion or percentage of deployments that result in failure or errors. It indicates the rate at which changes negatively impact the stability or functionality of the system. CFR also provides a clear view of the platform’s quality and stability eg: how much effort goes into addressing problems and releasing code.

Lower CFR indicates that deployments are reliable, changes are thoroughly tested, and less likely to cause issues in production. Moreover, it also reflects a well-functioning development and deployment processes, boosting team confidence and morale. 

Mean Time to Restore

Mean Time to Restore (MTTR) represents the average time taken to resolve a production failure/incident and restore normal system functionality each week.  Low MTTR indicates that the platform is resilient, quickly recovers from issues, and efficiency of incident response. 

Faster recovery time minimizes the impact on users, increasing their satisfaction and trust in service. Moreover, it contributes to higher system uptime and availability and enhances your platform’s reputation, giving you a competitive edge. 

Resource Utilization 

This KPI tracks the usage of system resources. It is a critical metric that optimizes resource allocation and cost efficiency. Resource Utilization balances several objectives with a fixed amount of resources. 

It allows platform engineers to distribute limited resources evenly and efficiently and understand where exactly to spend. Resource Utilization also aids in capacity planning and helps in avoiding potential bottlenecks. 

Error Rates

Error Rates measure the number of errors encountered in the platform. It identifies the stability, reliability, and user experience of the platform. High Error Rates indicate underlying problems that need immediate attention which can otherwise, degrade user experience, leading to frustration and potential loss of users.

Monitoring Error Rates helps in the early detection of issues, enabling proactive response, and preventing minor issues from escalating into major outages. It also provides valuable insights into system performance and creates a feedback loop that informs continuous improvement efforts. 

Team Velocity 

Team Velocity is a critical metric that measures the amount of work completed in a given iteration (e.g., sprint). It highlights the developer productivity and efficiency as well as in planning and prioritizing future tasks. 

It helps to forecast the completion dates of larger projects or features, aiding in long-term planning and setting stakeholder expectations. Team Velocity also helps to understand the platform teams’ capacity to evenly distribute tasks and prevent overloading team members. 

How to Develop a Platform Engineering KPI Plan? 

Define Objectives 

Firstly, ensure that the KPIs support the organization’s broader objectives. A few of them include improving system reliability, enhancing user experience, or increasing development efficiency. Always focus on metrics that reflect the unique aspects of platform engineering. 

Identify Key Performance Indicators 

Select KPIs that provide a comprehensive view of platform engineering performance. We’ve shared some critical KPIs above. Choose those KPIs that fit your objectives and other considered factors. 

Establish Baseline and Targets

Assess current performance levels of software engineers to establish baselines. Set targets and ensure they are realistic and achievable for each KPI. They must be based on historical data, industry benchmarks, and business objectives.

Analyze and Interpret Data

Regularly analyze trends in the data to identify patterns, anomalies, and areas for improvement. Set up alerts for critical KPIs that require immediate attention. Don’t forget to conduct root cause analysis for any deviations from expected performance to understand underlying issues.

Review and Refine KPIs

Lastly, review the relevance and effectiveness of the KPIs periodically to ensure they align with business objectives and provide value. Adjust targets based on changes in business goals, market conditions, or team capacity.

Typo - An Effective Platform Engineering Tool 

Typo is an effective platform engineering tool 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.

‍

Conclusion

Monitoring the right KPIs is essential for successful platform teams. By treating your platform as an internal product and your teams as customers, you can focus on delivering value and driving continuous improvement. The KPIs discussed above, provide a comprehensive view of your platform's performance and areas for enhancement. 

There are other KPIs available as well that we have not mentioned. Do your research and consider those that best suit your team and objectives.

All the best! 

‍

Developer productivity is the new buzzword across the industry. Suddenly, measuring developer productivity has started going mainstream after the remote work culture, and companies like McKinsey are publishing articles titled - ”Yes, you can measure software developer productivity” causing a stir in the software development community, So we thought we should share our take on- Developer Productivity.

We will be covering the following Whats, Whys & Hows about Developer Productivity in this piece-

• What is developer productivity?
• Why do we need to measure developer productivity?
• How do we measure it at the Team and individual level? & Why is it more complicated to measure developer productivity than Sales or Hiring productivity?
• Challenges & Dangers of measuring developer productivity & What not to measure.
• What is the impact of measuring developer productivity on engineering culture?

What is Developer Productivity?

Developer productivity refers to the effectiveness and efficiency with which software developers create high-quality software that meets business goals. It encompasses various dimensions, including code quality, development speed, team collaboration, and adherence to best practices. For engineering managers and leaders, understanding developer productivity is essential for driving continuous improvement and achieving successful project outcomes.

Key Aspects of Developer Productivity

Quality of Output: Developer productivity is not just about the quantity of code or code changes produced; it also involves the quality of that code. High-quality code is maintainable, readable, and free of significant bugs, which ultimately contributes to the overall success of a project.

Development Speed: This aspect measures how quickly developers (usually referred as developer velocity) can deliver features, fixes, and updates. While developer velocity is important, it should not come at the expense of code quality. Effective engineering teams strike a balance between delivering quickly and maintaining high standards.

Collaboration and Team Dynamics: Successful software development relies heavily on effective teamwork. Collaboration tools and practices that foster communication and knowledge sharing can significantly enhance developer productivity. Engineering managers should prioritize creating a collaborative environment that encourages teamwork.

Adherence to Best Practices for Outcomes: Following coding standards, conducting code review, and implementing testing protocols are essential for maintaining development productivity. These practices ensure that developers produce high-quality work consistently, which can lead to improved project outcomes.

Why do we need to measure dev productivity?

We all know that no love to be measured but the CEOs & CFOs have an undying love for measuring the ROI of their teams, which we can't ignore. The more the development productivity, the more the RoI. However, measuring developer productivity is essential for engineering managers and leaders too who want to optimize their teams' performance- We can't improve something that we don't measure.

Understanding how effectively developers work can lead to improved project outcomes, better resource allocation, and enhanced team morale. In this section, we will explore the key reasons why measuring developer productivity is crucial for engineering management.

Enhancing Team Performance

Measuring developer productivity allows engineering managers to identify strengths and weaknesses within their teams. By analyzing developer productivity metrics, leaders can pinpoint areas where new developer excel and where they may need additional support or resources. This insight enables managers to tailor training programs, allocate tasks more effectively, and foster a culture of continuous improvement.

‍

‍

Driving Business Outcomes

Developer productivity is directly linked to business success. By measuring development team productivity, managers can assess how effectively their teams deliver features, fix bugs, and contribute to overall project goals. Understanding productivity levels helps align development efforts with business objectives, ensuring that the team is focused on delivering value that meets customer needs.

Improving Resource Allocation

Effective measurement of developer productivity enables better resource allocation. By understanding how much time and effort are required for various tasks, managers can make informed decisions about staffing, project timelines, and budget allocation. This ensures that resources are utilized efficiently, minimizing waste and maximizing output.

Fostering a Positive Work Environment

Measuring developer productivity can also contribute to a positive work environment. By recognizing high-performing teams and individuals, managers can boost morale and motivation. Additionally, understanding productivity trends can help identify burnout or dissatisfaction, allowing leaders to address issues proactively and create a healthier workplace culture.

‍

Facilitating Data-Driven Decisions

In today’s fast-paced software development landscape, data-driven decision-making is essential. Measuring developer productivity provides concrete data that can inform strategic decisions. Whether it's choosing new tools, adopting agile methodologies, or implementing process changes, having reliable developer productivity metrics allows managers to make informed choices that enhance team performance.

‍

‍

Encouraging Collaboration and Communication

Regularly measuring productivity can highlight the importance of collaboration and communication within teams. By assessing metrics related to teamwork, such as code reviews and pair programming sessions, managers can encourage practices that foster collaboration. This not only improves productivity but overall developer experience by strengthening team dynamics and knowledge sharing.

Ultimately, understanding developer experience and measuring developer productivity leads to better outcomes for both the team and the organization as a whole.

How do we measure Developer Productivity?

Measuring developer productivity is essential for engineering managers and leaders who want to optimize their teams' performance.

Strategies for Measuring Productivity

Focus on Outcomes, Not Outputs: Shift the emphasis from measuring outputs like lines of code to focusing on outcomes that align with business objectives. This encourages developers to think more strategically about the impact of their work.

Measure at the Team Level: Assess productivity at the team level rather than at the individual level. This fosters team collaboration, knowledge sharing, and a focus on collective goals rather than individual competition.

Incorporate Qualitative Feedback: Balance quantitative metrics with qualitative feedback from developers through surveys, interviews, and regular check-ins. This provides valuable context and helps identify areas for improvement.

Encourage Continuous Improvement: Position productivity measurement as a tool for continuous improvement rather than a means of evaluation. Encourage developers to use metrics to identify areas for growth and work together to optimize workflows and development processes.

Lead by Example: As engineering managers and leaders, model the behavior you want to see in your team & team members. Prioritize work-life balance, encourage risk-taking and innovation, and create an environment where developers feel supported and empowered.

Measuring Dev productivity involves assessing both team and individual contributions to understand how effectively developers are delivering value through their development processes. Here’s how to approach measuring productivity at both levels:

Team-Level Developer Productivity

Measuring productivity at the team level provides a more comprehensive view of how collaborative efforts contribute to project success. Here are some effective metrics:

DORA Metrics

The DevOps Research and Assessment (DORA) metrics are widely recognized for evaluating team performance. Key metrics include:

• Deployment Frequency: How often the software engineering team releases code to production.
• ‍Lead Time for Changes: The time taken for committed code to reach production.
• ‍Change Failure Rate: The percentage of deployments that result in failures.
• ‍Time to Restore Service: The time taken to recover from a failure.

Issue Cycle Time‍

This metric measures the time taken from the start of work on a task to its completion, providing insights into the efficiency of the software development process.

Team Satisfaction and Engagement‍

Surveys and feedback mechanisms can gauge team morale and satisfaction, which are critical for long-term productivity.

Collaboration Metrics‍

Assessing the frequency and quality of code reviews, pair programming sessions, and communication can provide insights into how well the software engineering team collaborates.

Individual Developer Productivity

While team-level metrics are crucial, individual developer productivity also matters, particularly for performance evaluations and personal development. Here are some metrics to consider:

• Pull Requests and Code Reviews: Tracking the number of pull requests submitted and the quality of code reviews can provide insights into an individual developer's engagement and effectiveness.

• Commit Frequency: Measuring how often a developer commits code can indicate their active participation in projects, though it should be interpreted with caution to avoid incentivizing quantity over quality.

• Personal Goals and Outcomes: Setting individual objectives related to project deliverables and tracking their completion can help assess individual productivity in a meaningful way.

• Skill Development: Encouraging developers to pursue training and certifications can enhance their skills, contributing to overall productivity.

Measuring developer productivity metrics presents unique challenges compared to more straightforward metrics used in sales or hiring. Here are some reasons why:

• Complexity of Work: Software development involves intricate problem-solving, creativity, and collaboration, making it difficult to quantify contributions accurately. Unlike sales, where metrics like revenue generated are clear-cut, developer productivity encompasses various qualitative aspects that are harder to measure for project management.

• Collaborative Nature: Development work is highly collaborative. Team members often intertwine with team efforts, making it challenging to isolate the impact of one developer's work. In sales, individual performance is typically more straightforward to assess based on personal sales figures.

• Inadequate Traditional Metrics: Traditional metrics such as Lines of Code (LOC) and commit frequency often fail to capture the true essence of developer productivity of a pragmatic engineer. These metrics can incentivize quantity over quality, leading developers to produce more code without necessarily improving the software's functionality or maintainability. This focus on superficial metrics can distort the understanding of a developer's actual contributions.

• Varied Work Activities: Developers engage in various activities beyond coding, including debugging, code reviews, and meetings. These essential tasks are often overlooked in productivity measurements, whereas sales roles typically have more consistent and quantifiable activities.

• Productivity Tools and Software development Process: The developer productivity tools and methodologies used in software development are constantly changing, making it difficult to establish consistent metrics. In contrast, sales processes tend to be more stable, allowing for easier benchmarking and comparison.

By employing a balanced approach that considers both quantitative and qualitative factors, with a few developer productivity tools, engineering leaders can gain valuable insights into their teams' productivity and foster an environment of continuous improvement & better developer experience.

Challenges of measuring Developer Productivity - What not to Measure?

Measuring developer productivity is a critical task for engineering managers and leaders, yet it comes with its own set of challenges and potential pitfalls. Understanding these challenges is essential to avoid the dangers of misinterpretation and to ensure that developer productivity metrics genuinely reflect the contributions of developers. In this section, we will explore the challenges of measuring developer productivity and highlight what not to measure.

Challenges of Measuring Developer Productivity

• Complexity of Software Development: Software development is inherently complex, involving creativity, problem-solving, and collaboration. Unlike more straightforward fields like sales, where performance can be quantified through clear metrics (e.g., sales volume), developer productivity is multifaceted and includes various non-tangible elements. This complexity makes it difficult to establish a one-size-fits-all metric.

• Inadequate Traditional Metrics: Traditional metrics such as Lines of Code (LOC) and commit frequency often fail to capture the true essence of developer productivity. These metrics can incentivize quantity over quality, leading developers to produce more code without necessarily improving the software's functionality or maintainability. This focus on superficial metrics can distort the understanding of a developer's actual contributions.

• Team Dynamics and Collaboration: Measuring individual productivity can overlook the collaborative nature of software development. Developers often work in teams where their contributions are interdependent. Focusing solely on individual metrics may ignore the synergistic effects of collaboration, mentorship, and knowledge sharing, which are crucial for a team's overall success.

• Context Ignorance: Developer productivity metrics often fail to consider the context in which developers work. Factors such as project complexity, team dynamics, and external dependencies can significantly impact productivity but are often overlooked in traditional assessments. This lack of context can lead to misleading conclusions about a developer's performance.

• Potential for Misguided Incentives: Relying heavily on specific metrics can create perverse incentives. For example, if developers are rewarded based on the number of commits, they may prioritize frequent small commits over meaningful contributions. This can lead to a culture of "gaming the system" rather than fostering genuine productivity and innovation.

What Not to Measure

• Lines of Code (LOC): While LOC can provide some insight into coding activity, it is not a reliable measure of productivity. More code does not necessarily equate to better software. Instead, focus on the quality and impact of the code produced.

• Commit Frequency: Tracking how often developers commit code can give a false sense of productivity. Frequent commits do not always indicate meaningful progress and can encourage developers to break down their work into smaller, less significant pieces.

• Bug Counts: Focusing on the number of bugs reported or fixed can create a negative environment where developers feel pressured to avoid complex tasks that may introduce bugs. This can stifle innovation and lead to a culture of risk aversion.

• Time Spent on Tasks: Measuring how long developers spend on specific tasks can be misleading. Developers may take longer on complex problems that require deep thinking and creativity, which are essential for high-quality software development.

Measuring developer productivity is fraught with challenges and dangers that engineering managers must navigate carefully. By understanding these complexities and avoiding outdated or superficial metrics, leaders can foster a more accurate and supportive environment for their development team productivity.

What is the impact of measuring Dev productivity on engineering culture?

Developer productivity improvements are a critical factor in the success of software development projects. As engineering managers or technology leaders, measuring and optimizing developer productivity is essential for driving development team productivity and delivering successful outcomes. However, measuring development productivity can have a significant impact on engineering culture & software engineering talent, which must be carefully navigated. Let's talk about measuring developer productivity while maintaining a healthy and productive engineering culture.

Measuring developer productivity presents unique challenges compared to other fields. The complexity of software development, inadequate traditional metrics, team dynamics, and lack of context can all lead to misguided incentives and decreased morale. It's crucial for engineering managers to understand these challenges to avoid the pitfalls of misinterpretation and ensure that developer productivity metrics genuinely reflect the contributions of developers.

Remember, the goal is not to maximize metrics but to create a development environment where software engineers can thrive and deliver maximum value to the organization.

Development teams using Typo experience a 30% improvement in Developer Productivity. Want to Try Typo?

‍

‍

Code review is all about improving the code quality. However, it can be a nightmare for developers when not done correctly. They may experience several code review challenges and slow down the entire development process. This further reduces their morale and efficiency and results in developer burnout.

Hence, optimizing the code review process is crucial for both code reviewers and developers. In this blog post, we have shared a few tips on optimizing code reviews to boost developer productivity.

Importance of Code Reviews

The Code review process is an essential stage in the software development life cycle. It has been a defining principle in agile methodologies. It ensures high-quality code and identifies potential issues or bugs before they are deployed into production.

Another notable benefit of code reviews is that it helps to maintain a continuous integration and delivery pipeline to ensure code changes are aligned with project requirements. It also ensures that the product meets the quality standards, contributing to the overall success of sprint or iteration.

With a consistent code review process, the development team can limit the risks of unnoticed mistakes and prevent a significant amount of tech debt.

They also make sure that the code meets the set acceptance criteria, and functional specifications and whether the code base follows consistent coding styles across the codebase.

Lastly, it provides an opportunity for developers to learn from each other and improve their coding skills which further helps in fostering continuous growth and helps raise the overall code quality.

How do Ineffective Code Reviews Decrease Developer Productivity?

Unclear Standards and Inconsistencies

When the code reviews lack clear guidelines or consistent criteria for evaluation, the developers may feel uncertain of what is expected from their end. This leads to ambiguity due to varied interpretations of code quality and style. It also takes a lot of their time to fix issues on different reviewers’ subjective opinions. This leads to frustration and decreased morale among developers.

Increase in Bottlenecks and Delays

When developers wait for feedback for an extended period, it prevents them from progressing. This slows down the entire software development lifecycle, resulting in missed deadlines and decreased morale. Hence, negatively affecting the deployment timeline, customer satisfaction, and overall business outcomes.

Low Quality and Delayed Feedback

When reviewers communicate vague, unclear, and delayed feedback, they usually miss out on critical information. This leads to context-switching for developers which makes them lose focus on their current tasks. Moreover, they need to refamiliarize themselves with the code when the review is completed. Hence, resulting in developers losing their productivity.

Increase Cognitive Load

Frequent switching between writing and reviewing code requires a lot of mental effort. This makes it harder for developers to be focused and productive. Poorly structured, conflicting, or unclear feedback also confuses developers on which of them to prioritize first and understand the rationale behind suggested changes. This slows down the progress, leading to decision fatigue and reducing the quality of work.

Knowledge Gaps and Lack of Context

Knowledge gaps usually arise when reviewers lack the necessary domain knowledge or context about specific parts of the codebase. This results in a lack of context which further misguides developers who may overlook important issues. They may also need extra time to justify their decision and educate reviewers.

How to Optimize Code Review Process to Improve Developer Productivity?

Set Clear Goals and Standards

Establish clear objectives, coding standards, and expectations for code reviews. Communicate in advance with developers such as how long reviews should take and who will review the code. This allows both reviewers and developers to focus their efforts on relevant issues and prevent their time being wasted on insignificant matters.

Use a Code Review Checklist

Code review checklists include a predetermined set of questions and rules that the team will follow during the code review process. A few of the necessary quality checks include:

• Readability and maintainability: This is the first criterion and cannot be overstated enough.

• Uniform formatting: Whether the code with consistent indentation, spacing, and naming convention easy to understand?

• Testing and quality assurance: Whether it have meticulous testing and quality assurance processes?

• Boundary testing: Are we exploring extreme scenarios and boundary conditions to identify hidden problems?

• Security and performance: Are we ensuring security and performance in our source code?

• Architectural integrity: Whether the code is scalable, sustainable, and has a solid architectural design?

Prioritize High-Impact Issues

The issues must be prioritized based on their severity and impact. Not every issue in the code review process is equally important. Take up those issues first which affect system performance, security, or major features. Review them more thoroughly rather than the ones that have smaller and less impactful changes. It helps in allocating time and resources effectively.

Encourage Constructive Feedback

Always share specific, honest, and actionable feedback with the developers. The feedback must point in the right direction and must explain the ‘why’ behind it. It will reduce follow-ups and give necessary context to the developers. This also helps the engineering team to improve their skills and produce better code which further results in a high-quality codebase.

Automate Wherever Possible

Use automation tools such as style check, syntax check, and static code analysis tools to speed up the review process. This allows for routine checks for style, syntax errors, potential bugs, and performance issues and reduces the manual effort needed on such tasks. Automation allows developers to focus on more complex issues and allocate time more effectively.

Keep Reviews Small and Focused

Break down code into smaller, manageable chunks. This will be less overwhelming and time-consuming. The code reviewers can concentrate on details, adhere to the style guide and coding standards, and identify potential bugs. This will allow them to provide meaningful feedback more effectively. This helps in a deeper understanding of the code’s impact on the overall project.

Recognize and Reward Good Work

Acknowledge and celebrate developers who consistently produce high-quality code. This enables developers to feel valued for their contributions, leading to increased engagement, job satisfaction, and a sense of ownership in the project’s success. They are also more likely to continue producing high-quality code and actively participate in the review process.

Encourage Pair Programming or Pre-Review

Encourage pair programming or pre-review sessions to by enabling real-time feedback, reducing review time, and improving code quality. This fosters collaboration, enhances knowledge sharing, and helps catch issues early. Hence, leading to smoother and more effective reviews. It also promotes team bonding, streamlines communication, and cultivates a culture of continuous learning and improvement.

Use a Software Engineering Analytics Platform

Using an Engineering analytics platform in an organization is a powerful way to optimize the code review process and improve developer productivity. It provides comprehensive insights into the code quality, technical debt, and bug frequency which allow teams to proactively identify bottlenecks and address issues in real time before they escalate. It also allow teams to monitor their practices continuously and make adjustments as needed.

Typo — Automated Code Review Tool

Typo’s automated code review tool identifies issues in your code and auto-fixes them before you merge to master. This means less time reviewing and more time for important tasks. It keeps your code error-free, making the whole process faster and smoother.

Key Features

• Supports top 8 languages including C++ and C#.

• Understands the context of the code and fixes issues accurately.

• Optimizes code efficiently.

• Provides automated debugging with detailed explanations.

• Standardizes code and reduces the risk of a security breach

‍

‍

‍

Learn More About Typo

Conclusion

If you prioritize the code review process, follow the above-mentioned tips. It will help in maximizing code quality, improve developer productivity, and streamline the development process.

Happy reviewing!

‍

In the crazy world of software development, getting developers to be productive is like finding the Holy Grail for tech companies. When developers hit their stride, turning out valuable work at breakneck speed, it’s a win for everyone. But let’s be honest—traditional productivity metrics, like counting lines of code or tracking hours spent fixing bugs, are about as helpful as a screen door on a submarine.

Say hello to the SPACE framework: your new go-to for cracking the code on developer productivity. This approach doesn’t just dip a toe in the water—it dives in headfirst to give you a clear, comprehensive view of how your team is doing. With the SPACE framework, you’ll ensure your developers aren’t just busy—they’re busy being awesome and delivering top-quality work on the dot. So buckle up, because we’re about to take your team’s productivity to the next level!

Introduction to the SPACE Framework

The SPACE framework is a modern approach to measuring developer productivity, introduced in a 2021 paper by experts from GitHub and Microsoft Research. This framework goes beyond traditional metrics to provide a more accurate and holistic view of productivity.

Nicole Forsgren, the lead author, emphasizes that measuring productivity by lines of code or speed can be misleading. The SPACE framework integrates several key metrics to give a complete picture of developer productivity.

Detailed Breakdown of SPACE Metrics

The five SPACE framework dimensions are:

Satisfaction and Well-being

When developers are happy and healthy, they tend to be more productive. If they enjoy their work and maintain a good work-life balance, they're more likely to produce high-quality results. On the other hand, dissatisfaction and burnout can severely hinder productivity. For example, a study by Haystack Analytics found that during the COVID-19 pandemic, 81% of software developers experienced burnout, which significantly impacted their productivity. The SPACE framework encourages regular surveys to gauge developer satisfaction and well-being, helping you address any issues promptly.

Performance

Traditional metrics often measure performance by the number of features added or bugs fixed. However, this approach can be problematic. According to the SPACE framework, performance should be evaluated based on outcomes rather than output. This means assessing whether the code reliably meets its intended purpose, the time taken to complete tasks, customer satisfaction, and code reliability.

Activity

Activity metrics are commonly used to gauge developer productivity because they are easy to quantify. However, they only provide a limited view. Developer Activity is the count of actions or outputs completed over time, such as coding new features or conducting code reviews. While useful, activity metrics alone cannot capture the full scope of productivity.

Nicole Forsgren points out that factors like overtime, inconsistent hours, and support systems also affect activity metrics. Therefore, it's essential to consider routine tasks like meetings, issue resolution, and brainstorming sessions when measuring activity.

Collaboration and Communication

Effective communication and collaboration are crucial for any development team's success. Poor communication can lead to project failures, as highlighted by 86% of employees in a study who cited ineffective communication as a major reason for business failures. The SPACE framework suggests measuring collaboration through metrics like the discoverability of documentation, integration speed, quality of work reviews, and network connections within the team.

Efficiency and Flow

Flow is a state of deep focus where developers can achieve high levels of productivity. Interruptions and distractions can break this flow, making it challenging to return to the task at hand. The SPACE framework recommends tracking metrics such as the frequency and timing of interruptions, the time spent in various workflow stages, and the ease with which developers maintain their flow.

Benefits of the SPACE Framework

The SPACE framework offers several advantages over traditional productivity metrics. By considering multiple dimensions, it provides a more nuanced view of developer productivity. This comprehensive approach helps avoid the pitfalls of single metrics, such as focusing solely on lines of code or closed tickets, which can lead to gaming the system.

Moreover, the SPACE framework allows you to measure both the quantity and quality of work, ensuring that developers deliver high-quality software efficiently. This integrated view helps organizations make informed decisions about team productivity and optimize their workflows for better outcomes.

Implementing the SPACE Framework in Your Organization

Implementing the SPACE productivity framework effectively requires careful planning and execution. Below is a comprehensive plan and roadmap to guide you through the process. This detailed guide will help you tailor the SPACE framework to your organization's unique needs and ensure a smooth transition to this advanced productivity measurement approach.

Step 1: Understanding Your Current State

Objective: Establish a baseline by understanding your current productivity measurement practices and developer workflow.

1. Conduct a Productivity Audit
   • Review existing metrics and tools like Typo used for tracking productivity. 
   • Identify gaps and limitations in current measurement methods.
   • Gather feedback from developers and managers on existing practices.
2. Analyze Team Dynamics and Workflow
   • Map out your development process, identifying key stages and tasks.
   • Observe how teams collaborate, communicate, and handle interruptions.
   • Assess the overall satisfaction and well-being of your developers.

Outcome: A comprehensive report detailing your current productivity measurement practices, team dynamics, and workflow processes.

Step 2: Setting Goals and Objectives

Objective: Define clear goals and objectives for implementing the SPACE framework.

1. Identify Key Business Objectives
   • Align the goals of the SPACE framework with your company's strategic objectives.
   • Focus on improving areas such as time-to-market, code quality, customer satisfaction, and developer well-being.
2. Set Specific, Measurable, Achievable, Relevant, and Time-bound (SMART) Goals
   • Example Goals
     • Increase developer satisfaction by 20% within six months.
     • Reduce average bug resolution time by 30% over the next quarter.
     • Improve code review quality scores by 15% within the next year.

Outcome: A set of SMART goals that will guide the implementation of the SPACE framework.

Step 3: Selecting and Customizing SPACE Metrics

Objective: Choose the most relevant SPACE metrics and customize them to fit your organization's needs.

1. Review SPACE Metrics
   • Satisfaction and Well-being
   • Performance
   • Activity
   • Collaboration and Communication
   • Efficiency and Flow
2. Customize Metrics
   • Tailor each metric to align with your organization's specific context and objectives.
   • Example Customizations
     • Satisfaction and Well-being: Conduct quarterly surveys to measure job satisfaction and work-life balance.
     • Performance: Track the reliability of code and customer feedback on delivered features.
     • Activity: Measure the number of completed tasks, code commits, and other relevant activities.
     • Collaboration and Communication: Monitor the quality of code reviews and the speed of integrating work.
     • Efficiency and Flow: Track the frequency and duration of interruptions and the time spent in flow states.

Outcome: A customized set of SPACE metrics tailored to your organization's needs.

Step 4: Implementing Measurement Tools and Processes

Objective: Implement tools and processes to measure and track the selected SPACE metrics.

1. Choose Appropriate Tools
   • Use project management tools like Jira or Trello to track activity and performance metrics.
   • Implement collaboration tools such as Slack, Microsoft Teams, or Confluence to facilitate communication and knowledge sharing.
   • Utilize code review tools like CodeIQ by Typo to monitor the quality of code and collaboration.
2. Set Up Data Collection Processes
   • Establish processes for collecting and analyzing data for each metric.
   • Ensure that data collection is automated wherever possible to reduce manual effort and improve accuracy.
3. Train Your Team
   • Provide training sessions for developers and managers on using the new tools and understanding the SPACE metrics.
   • Encourage open communication and address any concerns or questions from the team.

Outcome: A fully implemented set of tools and processes for measuring and tracking SPACE metrics.

Step 5: Regular Monitoring and Review

Objective: Continuously monitor and review the metrics to ensure ongoing improvement.

1. Establish Regular Review Cycles
   • Conduct monthly or quarterly reviews of the SPACE metrics to track progress towards goals.
   • Hold team meetings to discuss the results, identify areas for improvement, and celebrate successes.
2. Analyze Trends and Patterns
   • Look for trends and patterns in the data to gain insights into team performance and productivity.
   • Use these insights to make informed decisions and adjustments to workflows and processes.
3. Solicit Feedback
   • Regularly gather feedback from developers and managers on the effectiveness of the SPACE framework.
   • Use this feedback to make continuous improvements to the framework and its implementation.

Outcome: A robust monitoring and review process that ensures the ongoing effectiveness of the SPACE framework.

Step 6: Continuous Improvement and Adaptation

Objective: Adapt and improve the SPACE framework based on feedback and evolving needs.

1. Iterate and Improve
   • Continuously refine and improve the SPACE metrics based on feedback and observed results.
   • Adapt the framework to address new challenges and opportunities as they arise.
2. Foster a Culture of Continuous Improvement
   • Encourage a culture of continuous improvement within your development teams.
   • Promote openness to change and a willingness to experiment with new ideas and approaches.
3. Share Success Stories
   • Share success stories and best practices with the broader organization to demonstrate the value of the SPACE framework.
   • Use these stories to inspire other teams and encourage the adoption of the framework across the organization.

Outcome: A dynamic and adaptable SPACE framework that evolves with your organization's needs.

Conclusion

Implementing the SPACE framework is a strategic investment in your organization's productivity and success. By following this comprehensive plan and roadmap, you can effectively integrate the SPACE metrics into your development process, leading to improved performance, satisfaction, and overall productivity. Embrace the journey of continuous improvement and leverage the insights gained from the SPACE framework to unlock the full potential of your development teams.

‍

In today’s fast-paced software development world, understanding and improving developer productivity is more crucial than ever. One framework that has gained prominence for its comprehensive approach to measuring and enhancing productivity is the SPACE Framework. This framework, developed by industry experts and backed by extensive research, offers a multi-dimensional perspective on productivity that transcends traditional metrics.

This blog delves deep into the genesis of the SPACE Framework, its components, and how it can be effectively implemented to boost developer productivity. We’ll also explore real-world success stories of companies that have benefited from adopting this framework.

The genesis of the SPACE Framework

The SPACE Framework was introduced by researchers Nicole Forsgren, Margaret-Anne Storey, Chandra Maddila, Thomas Zimmermann, Brian Houck, and Jenna Butler. Their work was published in a paper titled “The SPACE of Developer Productivity: There’s More to it than You Think!” emphasising that a single metric cannot measure developer productivity. Instead, it should be viewed through multiple lenses to capture a holistic picture.

Components of the SPACE Framework

The SPACE Framework is an acronym that stands for:

1. Satisfaction and Well-being
2. Performance
3. Activity
4. Communication and Collaboration
5. Efficiency and Flow

Each component represents a critical aspect of developer productivity, ensuring a balanced approach to measurement and improvement.

Detailed breakdown of the SPACE Framework

1. Satisfaction and Well-being

Definition: This dimension focuses on how satisfied and happy developers are with their work and environment. It also considers their overall well-being, which includes factors like work-life balance, stress levels, and job fulfillment.

Why It Matters: Happy developers are more engaged, creative, and productive. Ensuring high satisfaction and well-being can reduce burnout and turnover, leading to a more stable and effective team.

Metrics to Consider:

• Employee satisfaction surveys
• Work-life balance scores
• Burnout indices
• Turnover rates

2. Performance

Definition: Performance measures the outcomes of developers’ work, including the quality and impact of the software they produce. This includes assessing code quality, deployment frequency, and the ability to meet user needs.

Why It Matters: High performance indicates that the team is delivering valuable software efficiently. It helps in maintaining a competitive edge and ensuring customer satisfaction.

Metrics to Consider:

• Code quality metrics (e.g., number of bugs, code review scores)
• Deployment frequency
• Customer satisfaction ratings
• Feature adoption rates

3. Activity

Definition: Activity tracks the actions developers take, such as the number of commits, code reviews, and feature development. This component focuses on the volume and types of activities rather than their outcomes.

Why It Matters: Monitoring activity helps understand workload distribution and identify potential bottlenecks or inefficiencies in the development process.

Metrics to Consider:

• Number of commits per developer
• Code review participation
• Task completion rates
• Meeting attendance

4. Communication and Collaboration

Definition: This dimension assesses how effectively developers interact with each other and with other stakeholders. It includes evaluating the quality of communication channels and collaboration tools used.

Why It Matters: Effective communication and collaboration are crucial for resolving issues quickly, sharing knowledge, and fostering a cohesive team environment. Poor communication can lead to misunderstandings and project delays.

Metrics to Consider:

• Frequency and quality of team meetings
• Use of collaboration tools (e.g., Slack, Jira)
• Cross-functional team interactions
• Feedback loops

5. Efficiency and Flow

Definition: Efficiency and flow measure how smoothly the development process operates, including how well developers can focus on their tasks without interruptions. It also looks at the efficiency of the processes and tools in place.

Why It Matters: High efficiency and flow indicate that developers can work without unnecessary disruptions, leading to higher productivity and job satisfaction. It also helps in identifying and eliminating waste in the process.

Metrics to Consider:

• Cycle time (time from task start to completion)
• Time spent in meetings vs. coding
• Context switching frequency
• Tool and process efficiency

Implementing the SPACE Framework in real life

Implementing the SPACE Framework requires a strategic approach, involving the following steps:

Establish baseline metrics

Before making any changes, establish baseline metrics for each SPACE component. Use existing tools and methods to gather initial data.

Actionable Steps:

• Conduct surveys to measure satisfaction and well-being.
• Use code quality tools to assess performance.
• Track activity through version control systems.
• Analyze communication patterns via collaboration tools.
• Measure efficiency and flow using project management software.

Set clear goals

Define what success looks like for each component of the SPACE Framework. Set achievable and measurable goals.

Actionable Steps:

• Increase employee satisfaction scores by 10% within six months.
• Reduce bug rates by 20% over the next quarter.
• Improve code review participation by 15%.
• Enhance cross-team communication frequency.
• Shorten cycle time by 25%.

Implement changes

Based on the goals set, implement changes to processes, tools, and practices. This may involve adopting new tools, changing workflows, or providing additional training.

Actionable Steps:

• Introduce well-being programs to improve satisfaction.
• Adopt automated testing tools to enhance performance.
• Encourage regular code reviews to boost activity.
• Use collaboration tools like Slack or Microsoft Teams to improve communication.
• Streamline processes to reduce context switching and improve flow.

Monitor and adjust

Regularly monitor the metrics to evaluate the impact of the changes. Be prepared to make adjustments as necessary to stay on track with your goals.

Actionable Steps:

• Use dashboards to track key metrics in real time.
• Hold regular review meetings to discuss progress.
• Gather feedback from developers to identify areas for improvement.
• Make iterative changes based on data and feedback.

Integrating the SPACE Framework with DORA Metrics

Real-world success stories

GitHub

GitHub implemented the SPACE Framework to enhance its developer productivity. By focusing on communication and collaboration, they improved their internal processes and tools, leading to a more cohesive and efficient development team. They introduced regular team-building activities and enhanced their internal communication tools, resulting in a 15% increase in developer satisfaction and a 20% reduction in project completion time.

Microsoft

Microsoft adopted the SPACE Framework across several development teams. They focused on improving efficiency and flow by reducing context switching and streamlining their development processes. This involved adopting continuous integration and continuous deployment (CI/CD) practices, which reduced cycle time by 30% and increased deployment frequency by 25%.

Key software engineering metrics mapped to the SPACE Framework

This table outlines key software engineering metrics mapped to the SPACE Framework, along with how they can be measured and implemented to improve developer productivity and overall team effectiveness.

What engineering leaders can do

Engineering leaders play a crucial role in the successful implementation of the SPACE Framework. Here are some actionable steps they can take:

Promote a culture of continuous improvement

Encourage a mindset of continuous improvement among the team. This involves being open to feedback and constantly seeking ways to enhance productivity and well-being.

Actionable Steps:

• Regularly solicit feedback from team members.
• Celebrate small wins and improvements.
• Provide opportunities for professional development and growth.

Invest in the right tools and processes

Ensure that developers have access to the tools and processes that enable them to work efficiently and effectively.

Actionable Steps:

• Conduct regular tool audits to ensure they meet current needs.
• Invest in training programs for new tools and technologies.
• Streamline processes to eliminate unnecessary steps and reduce bottlenecks.

Foster collaboration and communication

Create an environment where communication and collaboration are prioritized. This can lead to better problem-solving and more innovative solutions.

Actionable Steps:

• Organize regular team-building activities.
• Use collaboration tools to facilitate better communication.
• Encourage cross-functional projects to enhance team interaction.

Prioritize well-being and satisfaction

Recognize the importance of developer well-being and satisfaction. Implement programs and policies that support a healthy work-life balance.

Actionable Steps:

• Offer flexible working hours and remote work options.
• Provide access to mental health resources and support.
• Recognize and reward achievements and contributions.

Conclusion

The SPACE Framework offers a holistic and actionable approach to understanding and improving developer productivity. By focusing on satisfaction and well-being, performance, activity, communication and collaboration, and efficiency and flow, organizations can create a more productive and fulfilling work environment for their developers.

Implementing this framework requires a strategic approach, clear goal setting, and ongoing monitoring and adjustment. Real-world success stories from companies like GitHub and Microsoft demonstrate the potential benefits of adopting the SPACE Framework.

Engineering leaders have a pivotal role in driving this change. By promoting a culture of continuous improvement, investing in the right tools and processes, fostering collaboration and communication, and prioritizing well-being and satisfaction, they can significantly enhance developer productivity and overall team success.

In the software development industry, while user experience is an important aspect of the product life cycle, organizations are also considering Developer Experience.

A positive Developer Experience helps in delivering quality products and allows developers to be happy and healthy in the long run.

However, it is not always possible for organizations to measure and improve developer experience without any good tools and platforms.

What is Developer Experience?

Developer Experience is about the experience software developers have while working in the organization. It is the developers’ journey while working with a specific framework, programming languages, platform, documentation, general tools, and open-source solutions.

Positive Developer Experience = Happier teams

Developer Experience has a direct relationship with developer productivity. A positive experience results in high dev productivity, leading to high job satisfaction, performance, and morale. Hence, happier developer teams.

This starts with understanding the unique needs of developers and fostering a positive work culture for them.

Why Developer Experience is important?

Smooth onboarding process

Good DX ensures the onboarding process is as simple and smooth as possible. It includes making them familiar with the tools and culture and giving them the support they need to proceed further in their career. It also allows them to know other developers which helps in collaboration, open communication, and seeking help, whenever required.

Improves product quality

A positive Developer Experience leads to 3 effective C’s – Collaboration, communication, and coordination. Besides this, adhering to coding standards, best practices, and automated testing helps promote code quality and consistency and fix issues early.  As a result, development teams can easily create products that meet customer needs and are free from errors and glitches.  

Increases development speed

When Developer Experience is handled with care, software developers can work more smoothly and meet milestones efficiently. Access to well-defined tools, clear documents, streamlined workflow, and a well-configured development environment are few ways to boost development speed.  It also lets them minimize the need to switch between different tools and platforms which increases the focus and team productivity.

Attracts and retains top talents

Developers usually look out for a strong tech culture. So they can focus on their core skills and get acknowledged for their contributions. Great DX increases job satisfaction and aligns their values and goals with the organization. In return, developers bring the best to the table and want to stay in the organization for the long run.

Enhances collaboration

The right kind of Developer Experience encourages collaboration and effective communication tools. This fosters teamwork and reduces misunderstandings. Developers can easily discuss issues, share feedback, and work together on tasks. It helps streamline the development process and results in high-quality work.

Best developer experience tools

Time management tools

Clockwise

A powerful time management tool that streamlines and automates the calendar and protects developers’ flow time. It helps to strike a balance between meetings and coding time with a focus time feature.

Key features

• Seamlessly integrates with third-party applications such as Slack, Google Calendar, and Asana.
• Determines the most suitable meeting times for both developers and engineering leaders.
• Creates custom smart holds i.e. protected time throughout the hold.
• Reschedules the meetings that are marked as ‘Flexible’.
• Provides a quick summary of how much meetings and focus time was spent last week.

Toggle track

A straightforward time-tracking, reporting, and billing tool for software developers. It lets development teams view tracked team entries in a grid or calendar format.

Key features

• ‘Dashboard and Reporting’ feature offers in-depth analysis and lets engineering leaders create customized dashboards.
• Simple and easy-to-use interface.
• Preferable for those who avoid real-time tracking rather than track their time manually.
• Offers a PDF invoice template that can be downloaded easily.
• Includes optional Pomodoro setting that allows developers to take regular quick breaks.

Software development intelligence

Typo

Typo is an intelligent engineering management platform used for gaining visibility, removing blockers, and maximizing developer effectiveness. It gives a comparative view of each team’s performance across velocity, quality, and throughput. This tool can be integrated with the tech stack to deliver real-time insights. Git, Slack, Calenders, and CI/CD to name a few.

Key features

• Seamlessly integrates with third-party applications such as Git, Slack, Calenders, and CI/CD tools.
• ‘Sprint analysis’ feature allows for tracking and analyzing the team’s progress throughout a sprint.
• Offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.
• Offers engineering benchmark to compare the team’s results across industries.
• User-friendly interface.

Code intelligence tools

Sourcegraph (Cody)

An AI code-based assistant tool that provides code-specific information and helps in locating precise code based on natural language description, file names, or function names.

Key features

• Explain complex lines of code in simple language.
• Identifies bugs and errors in a codebase and provides suggestions.
• Offers documentation generation.
• Answers questions about existing code.
• Generates code snippets, fixes, and improves code.

GitHub Copilot

Developed by GitHub in collaboration with open AI, it uses an open AI codex for writing code quickly. It draws context from the code and suggests whole lines or complete functions that developers can accept, modify, or reject.

Key features

• Creates predictive lines of code from comments and existing patterns in the code.
• Generates code in multiple languages including Typescript, Javascript, Ruby, C++, and Python.
• Seamlessly integrates with popular editors such as Neovim, JetBrains IDEs, and Visual Studio.
• Create dictionaries of lookup data
• Writes test cases and code comments

Communication and collaboration

Slack

A widely used communication platform that enables developers to real-time communication and share files. It also allows team members to share and download files and create external links for people outside of the team.

Key features

• Seamlessly integrates with third-party applications such as Google Calendar, Hubspot, Clickup, and Salesforce.
• ‘Huddle’ feature that includes phone and video conferencing options.
• Accessible on both mobile and desktop (Application and browser).
• Offers ‘Channel’ feature i.e. similar to groups, team members can create projects, teams, and topics.
• Perfect for asynchronous communication and collaboration.

Project and task management

JIRA

A part of the Atlassian group, JIRA is an umbrella platform that includes JIRA software, JIRA core, and JIRA work management. It relies on the agile way of working and is purposely built for developers and engineers.

Key features

• Built for agile and scrum workflows.
• Offers Kanban view.
• JIRA dashboard helps users to plan projects, measure progress, and track due dates.
• Offers third-party integrations with other parts of Atlassian groups and third-party apps like Github, Gitlab, and Jenkins.
• Offers customizable workflow states and transitions for every issue type.

Linear

A project management and issue-tracking tool that is tailored for software development teams. It helps the team plan their projects and auto-close and auto-archive issues.

Key features

• Simple and straightforward UI.
• Easy to set up.
• Breaks larger tasks into smaller issues.
• Switches between list and board layout to view work from any angle.
• Quickly apply filters and operators to refine issue lists and create custom views.

Automated software testing

Lambda test

A cloud-based cross-browser testing platform that provides real-time testing on multiple devices and simulators. It is used to create and run both manual and automatic tests and functions via the Selenium Automation Grid.

Key features

• Seamlessly integrates with other testing frameworks and CI/CD tools.
• Offers detailed automated logs such as exception logs, command logs, and metadata.
• Runs parallel tests in multiple browsers and environments.
• Offers command screenshots and video recordings of the script execution.
• Facilitates responsive testing to ensure the application works well on various devices and screen sizes.

Postman

A widely used automation testing tool for API. It provides a streamlined process for standardizing API testing and monitoring it for usage and trend insights.

Key features

• Seamlessly integrates with CI/CD pipelines.
• Enable users to mimic real-world scenarios and assess API behavior under various conditions.
• Creates mock servers, and facilitates realistic simulations and comprehensive testing.
• Provides monitoring features to gain insights into API performance and usage trends.
• Friendly and easy-to-use interface equipped with code snippets.

Continuous integration/continuous deployment

Circle CI

Certified with FebRamp and SOC Type II compliant, It helps in achieving CI/CD in open-source and large-scale projects. Circle CI streamlines the DevOps process and automates builds across multiple environments.

Key features

• Seamlessly integrates with third-party applications with Bitbucket, GitHub, and GitHub Enterprise.  
• Tracks the status of projects and keeps tabs on build processes
• ‘Parallel testing’ feature helps in running tests in parallel across different executors.
• Allows a single process per project.
• Provides ways to troubleshoot problems and inspect things such as directory path, log files, and running processes

Documentation

Swimm

Specifically designed for software development teams. Swimm is an innovative cloud-based documentation tool that integrates continuous documentation into the development workflow.

Key features

• Seamlessly integrates with development tools such as GitHub, VSC, and JetBrains IDEs.
• ‘Auto-sync’ feature ensures the document stays up to date with changes in the codebase.
• Creates new documents, rewrites existing ones, or summarizes information.
• Creates tutorials and visualizations within the codebase for better understanding and onboarding new members.
• Analyzes the entire codebase, documentation sources, and data from enterprise tools.

Developer engagement

DevEx by Typo

A valuable tool for development teams that captures 360 views of developer experience and helps with early indicators of their well-being and actionable insights on the areas that need attention through signals from work patterns and continuous AI-driven pulse check-ins.

Key features

• Research-backed framework that captures parameters and uncovers real issues.
• In-depth insights are published on the dashboard.
• Combines data-driven insights with proactive monitoring and strategic intervention.
• Identifies the key priority areas affecting developer productivity and well-being.
• Sends automated alerts to identify burnout signs in developers at an early stage.

GetDX

A comprehensive insights platform that is founded by researchers behind the DORA and SPACE framework. It offers both qualitative and quantitative measures to give a holistic view of the organization.

Key features

• Provides a suite of tools that capture data from surveys and systems in real-time.
• Breaks down results based on personas.
• Streamlines developer onboarding with real-time insights.
• Contextualizes performance with 180,000+ industry benchmark samples.
• Uses advanced statistical analysis to identify the top opportunities.

Conclusion

Overall Developer Experience is crucial in today’s times. It facilitates effective collaboration within engineering teams, offers real-time feedback on workflow efficiency and early signs of burnout, and enables informed decision-making. By pinpointing areas for improvement, it cultivates a more productive and enjoyable work environment for developers.

There are various tools available in the market. We’ve curated the best Developer Experience tools for you. You can check other tools as well. Do your own research and see what fits right for you.

All the best!

The software development industry constantly evolves, and measuring developer productivity has become crucial to success. It is the key to achieving efficiency, quality, and innovation. However, measuring productivity is not a one-size-fits-all process. It requires a deep understanding of productivity in a development context and selecting the right metrics to reflect it accurately.

This guide will help you and your teams navigate the complexities of measuring dev productivity. It offers insights into the process’s nuances and equips teams with the knowledge and tools to optimize performance. By following the tips and best practices outlined in this guide, teams can improve their productivity and deliver better software.

What is Developer Productivity?

Development productivity extends far beyond the mere output of code. It encompasses a multifaceted spectrum of skills, behaviors, and conditions that contribute to the successful creation of software solutions. Technical proficiency, effective collaboration, clear communication, suitable tools, and a conducive work environment are all integral components of developer productivity. Recognizing and understanding these factors is fundamental to devising meaningful metrics and fostering a culture of continuous improvement.

Benefits of developer productivity

• Increased productivity allows developers to complete tasks more efficiently. It leads to shorter development cycles and quicker delivery of products or features to the market.

• Productivity developers can focus more on code quality, testing, and optimization, resulting in higher-quality software with fewer bugs and issues.

• Developers can accomplish more in less time, reducing development costs and improving the organization’s overall return on investment.

• Productive developers often experience less stress and frustration due to reduced workloads and smoother development processes that lead to higher job satisfaction and retention rates.

• With more time and energy available, developers can dedicate resources to innovation, continuous learning, experimenting with new technologies, and implementing creative solutions to complex problems.

Metrics for Measuring Developer Productivity

Measuring software developers’ productivity cannot be any arbitrary criteria. This is why there are several metrics in place that can be considered while measuring it. Here we can divide them into quantitative and qualitative metrics. Here is what they mean:

Quantitative Metrics

Lines of Code (LOC) Written

While counting lines of code isn’t a perfect measure of productivity, it can provide valuable insights into coding activity. A higher number of lines might suggest more work done, but it doesn’t necessarily equate to higher quality or efficiency. However, tracking LOC changes over time can help identify trends and patterns in development velocity. For instance, a sudden spike in LOC might indicate a burst of productivity or potentially code bloat, while a decline could signal optimization efforts or refactoring.

Time to Resolve Issues/Bugs

The swift resolution of issues and bugs is indicative of a team’s efficiency in problem-solving and code maintenance. Monitoring the time it takes to identify, address, and resolve issues provides valuable feedback on the team’s responsiveness and effectiveness. A shorter time to resolution suggests agility and proactive debugging practices, while prolonged resolution times may highlight bottlenecks in the development process or technical debt that needs addressing.

Number of Commits or Pull Requests

Active participation in version control systems, as evidenced by the number of commits or pull requests, reflects the level of engagement and contribution to the codebase. A higher number of commits or pull requests may signify active development and collaboration within the team. However, it’s essential to consider the quality, not just quantity, of commits and pull requests. A high volume of low-quality changes may indicate inefficiency or a lack of focus.

Code Churn

Code churn refers to the rate of change in a codebase over time. Monitoring code churn helps identify areas of instability or frequent modifications, which may require closer attention or refactoring. High code churn could indicate areas of the code that are particularly complex or prone to bugs, while low churn might suggest stability but could also indicate stagnation if accompanied by a lack of feature development or innovation. Furthermore, focusing on code changes allows teams to track progress and ensure that updates align with project goals while emphasizing quality code ensures that these changes maintain or improve the overall codebase integrity and performance.

Qualitative Metrics

Code Review Feedback

Effective code reviews are crucial for maintaining code quality and fostering a collaborative development environment in engineering org. Monitoring code review feedback, such as the frequency of comments, the depth of review, and the incorporation of feedback into subsequent iterations, provides insights into the team’s commitment to quality and continuous improvement. A culture of constructive feedback and iteration during code reviews indicates a quality-driven approach to development.

Team Satisfaction and Morale

High morale and job satisfaction among engineering teams are key indicators of a healthy and productive work environment. Happy and engaged teams tend to be more motivated, creative, and productive. Regularly measuring team satisfaction through surveys, feedback sessions, or one-on-one discussions helps identify areas for improvement and reinforces a positive culture that fosters teamwork, productivity, and collaboration.

Rate of Feature Delivery

Timely delivery of features is essential for meeting project deadlines and delivering value to stakeholders. Monitoring the rate of feature delivery, including the speed and predictability of feature releases, provides insights into the team’s ability to execute and deliver results efficiently. Consistently meeting or exceeding feature delivery targets indicates a well-functioning development process and effective project management practices.

Customer Satisfaction and Feedback

Ultimately, the success of development efforts is measured by the satisfaction of end-users. Monitoring customer satisfaction through feedback channels, such as surveys, reviews, and support tickets, provides valuable insights into the effectiveness of the software in delivering meaningful solutions. Positive feedback and high satisfaction scores indicate that the development team has successfully met user needs and delivered a product that adds value. Conversely, negative feedback or low satisfaction scores highlight areas for improvement and inform future development priorities.

Best Practices for Measuring Developer Productivity

While analyzing the metrics and measuring software developer productivity, here are some things you need to remember:

• Balance Quantitative and Qualitative Metrics: Combining both types of metrics provides a holistic view of productivity.

• Customize Metrics to Fit Team Dynamics: Tailor metrics to align with the development team’s unique objectives and working styles.

• Ensure Transparency and Clarity: Communicate clearly about the purpose and interpretation of metrics to foster trust and accountability.

• Iterate and Adapt Measurement Strategies: Continuously evaluate and refine measurement approaches based on feedback and evolving project requirements.

How does Generative AI Improve Developer Productivity?

Below are a few ways in which Generative AI can have a positive impact on developer productivity:

Focus on meaningful tasks: Generative AI tools take up tedious and repetitive tasks, allowing developers to give their time and energy to meaningful activities, resulting in productivity gains within the team members’ workflow.

Assist in their learning graph: Generative AI lets software engineers gain practical insights and examples from these AI tools and enhance team performance.

Assist in pair programming: Through Generative AI, developers can collaborate with other developers easily.

Increase the pace of software development: Generative AI helps in the continuous delivery of products and services and drives business strategy.

How does Typo Measure Developer Productivity?

There are many developer productivity tools available in the market for tech companies. One of the tools is Typo – the most comprehensive solution on the market.

Typo helps with early indicators of their well-being and actionable insights on the areas that need attention through signals from work patterns and continuous AI-driven pulse check-ins on the developer experience. It offers innovative features to streamline workflow processes, enhance collaboration, and boost overall productivity in engineering teams. It helps in measuring the overall team’s productivity while keeping individual’ strengths and weaknesses in mind.

Here are three ways in which Typo measures the team productivity:

Software Development Visibility

Typo provides complete visibility in software delivery. It helps development teams and engineering leaders to identify blockers in real time, predict delays, and maximize business impact. Moreover, it lets the team dive deep into key DORA metrics and understand how well they are performing across industry-wide benchmarks. Typo also enables them to get real-time predictive analysis of how time is performing, identify the best dev practices, and provide a comprehensive view across velocity, quality, and throughput.

Hence, empowering development teams to optimize their workflows, identify inefficiencies, and prioritize impactful tasks. This approach ensures that resources are utilized efficiently, resulting in enhanced productivity and better business outcomes.

‍

Code Quality Automation

Typo helps developers streamline the development process and enhance their productivity by identifying issues in your code and auto-fixing them before merging to master. This means less time reviewing and more time for important tasks hence, keeping code error-free, making the whole process faster and smoother. The platform also uses optimized practices and built-in methods spanning multiple languages. Besides this, it standardizes the code and enforces coding standards which reduces the risk of a security breach and boosts maintainability.

Since the platform automates repetitive tasks, it allows development teams to focus on high-quality work. Moreover, it accelerates the review process and facilitates faster iterations by providing timely feedback.  This offers insights into code quality trends and areas for improvement, fostering an engineering culture that supports learning and development.

Developer Experience

Typo helps with early indicators of developers’ well-being and actionable insights on the areas that need attention through signals from work patterns and continuous AI-driven pulse check-ins on the experience of the developers. It includes pulse surveys, built on a developer experience framework that triggers AI-driven pulse surveys.

Based on the responses to the pulse surveys over time, insights are published on the Typo dashboard. These insights help engineering managers analyze how developers feel at the workplace, what needs immediate attention, how many developers are at risk of burnout and much more.

Hence, by addressing these aspects, Typo’s holistic approach combines data-driven insights with proactive monitoring and strategic intervention to create a supportive and high-performing work environment. This leads to increased developer productivity and satisfaction.

‍

Track Developer Productivity Effectively

Measuring developers’ productivity is not straightforward, as it varies from person to person. It is a dynamic process that requires careful consideration and adaptability.

To achieve greater success in software development, the development teams must embrace the complexity of productivity, select appropriate metrics, use relevant tools, and develop a supportive work culture.

There are many developer productivity tools available in the market. Typo stands out to be the prevalent one. It’s important to remember that the journey toward productivity is an ongoing process, and each iteration presents new opportunities for growth and innovation.

‍

As technology rapidly advances, software engineering is becoming an increasingly fast-paced field where maximizing productivity is critical for staying competitive and driving innovation. Efficient resource allocation, streamlined processes, and effective teamwork are all essential components of engineering productivity. In this guide, we will delve into the significance of measuring and improving engineering productivity, explore key metrics, provide strategies for enhancement, and examine the consequences of neglecting productivity tracking.

What is Engineering Productivity?

Engineering productivity refers to the efficiency and effectiveness of engineering teams in producing work output within a specified timeframe while maintaining high-quality standards. It encompasses various factors such as resource utilization, task completion speed, deliverable quality, and overall team performance. Essentially, engineering productivity measures how well a team can translate inputs like time, effort, and resources into valuable outputs such as completed projects, software features, or innovative solutions.

Tracking software engineering productivity involves analyzing key metrics like productivity ratio, throughput, cycle time, and lead time. By assessing these metrics, engineering managers can pinpoint areas for improvement, make informed decisions, and implement strategies to optimize productivity and achieve project objectives. Ultimately, engineering productivity plays a critical role in ensuring the success and competitiveness of engineering projects and organizations in today’s fast-paced technological landscape.

Why does Engineering Productivity Matter?

Impact on Project Timelines and Deadlines

Engineering productivity directly affects project timelines and deadlines. When teams are productive, they can deliver projects on schedule, meeting client expectations and maintaining stakeholder satisfaction.

Influence on Product Quality and Customer Satisfaction

High productivity levels correlate with better product quality. By maximizing productivity, engineering teams can focus on thorough testing, debugging, and refining processes, ultimately leading to increased customer satisfaction.

Role in Resource Allocation and Cost-Effectiveness

Optimized engineering productivity ensures efficient resource allocation, reducing unnecessary expenditures and maximizing ROI. By utilizing resources effectively, tech companies can achieve their goals within budgetary constraints.

The Importance of Tracking Engineering Productivity

Insights for Performance Evaluation and Improvement

Tracking engineering productivity provides valuable insights into team performance. By analyzing productivity metrics, organizations can identify areas for improvement and implement targeted strategies for enhancement.

Facilitates Data-Driven Decision-Making

Data-driven decision-making is essential for optimizing engineering productivity. Organizations can make informed decisions about resource allocation, process optimization, and project prioritization by tracking relevant metrics.

Helps in Setting Realistic Goals and Expectations

Tracking productivity metrics allows organizations to set realistic goals and expectations. By understanding historical productivity data, teams can establish achievable targets and benchmarks for future projects.

Factors Affecting Engineering Productivity

Team Dynamics and Collaboration

Effective teamwork and collaboration are essential for maximizing engineering productivity. Organizations can leverage team members’ diverse skills and expertise to achieve common goals by fostering a collaboration and communication culture.

Work Environment and Organizational Culture

The work environment and organizational culture play a significant role in determining engineering productivity. A supportive and conducive work environment fosters team members’ creativity, innovation, and productivity.

Resource Allocation and Workload Management

Efficient resource allocation and workload management are critical for optimizing engineering productivity. By allocating resources effectively and balancing workload distribution, organizations can ensure that team members work on tasks that align with their skills and expertise.

Strategies to Improve Engineering Productivity

Identifying Productivity Roadblocks and Bottlenecks

Identifying and addressing productivity roadblocks and bottlenecks is essential for improving engineering productivity. By conducting thorough assessments of workflow processes, organizations can identify inefficiencies, focus on workload distribution, and implement targeted solutions for improvement.

Implementing Effective Tools and Practices for Optimization

Leveraging effective tools and best practices is crucial for optimizing engineering productivity. By adopting agile methodologies, DevOps practices, and automation tools, engineering organizations can streamline processes, reduce manual efforts, enhance code quality, and accelerate delivery timelines.

Prioritizing Tasks Strategically

Strategic task prioritization, along with effective time management and goal setting, is key to maximizing engineering productivity. By prioritizing tasks based on their impact and urgency, organizations can ensure that team members focus on the most critical activities, leading to improved productivity and efficiency.

Promoting Collaboration and Communication

Promoting collaboration and communication within engineering teams is essential for maximizing productivity. By fostering open communication channels, encouraging knowledge sharing, and facilitating cross-functional collaboration, organizations can leverage the collective expertise of team members to drive innovation, and motivation and achieve common goal setting.

Continuous Improvement through Feedback Loops and Iteration

Continuous improvement is essential for maintaining and enhancing engineering productivity. By soliciting feedback from team members, identifying areas for improvement, and iteratively refining processes, organizations can continuously optimize productivity, address technical debt, and adapt to changing requirements and challenges.

Consequences of Not Tracking Engineering Productivity

Risk of Missed Deadlines and Project Delays

Neglecting to track engineering productivity increases the risk of missed deadlines and project delays. Without accurate productivity tracking, organizations may struggle to identify and address issues that could impact project timelines and deliverables.

Decreased Product Quality and Customer Dissatisfaction

Poor engineering productivity can lead to decreased product quality and customer dissatisfaction. Organizations may overlook critical quality issues without effective productivity tracking, resulting in negative business outcomes, subpar products, and unsatisfied customers.

Inefficient Resource Allocation and Higher Costs

Failure to track engineering productivity can lead to inefficient resource allocation and higher costs. Without visibility into productivity metrics, organizations may allocate resources ineffectively, wasting time, effort, and budgetary overruns.

Best Practices for Engineering Productivity

Setting SMART Goals

Setting SMART (specific, measurable, achievable, relevant, time-bound) goals is essential for maximizing engineering productivity. By setting clear and achievable goals, organizations can focus their efforts on activities that drive meaningful results and contribute to overall project success.

Establishing a Culture of Accountability and Ownership

Establishing a culture of accountability and ownership is critical for maximizing engineering productivity. Organizations can foster a sense of ownership and commitment that drives productivity and excellence by empowering team members to take ownership of their work and be accountable for their actions.

Promoting Work-Life Balance

Ensure work-life balance at the organization by promoting policies that support flexible schedules, encouraging regular breaks, and providing opportunities for professional development and personal growth. This can help reduce stress and prevent burnout, leading to higher productivity and job satisfaction.

Embracing Automation and Technology

Embracing automation and technology is key to streamlining processes and accelerating delivery timelines. By leveraging automation tools, DevOps practices, and advanced technologies, organizations can automate repetitive tasks, reduce manual efforts, and improve overall productivity and efficiency.

Investing in Employee Training and Skill Development

Investing in employee training and skill development is essential for maintaining and enhancing engineering productivity. By providing ongoing training and development opportunities, organizations can equip team members with the skills and knowledge they need to excel in their roles and contribute to overall project success.

Using Typo for Improved Engineering Productivity

Typo offers innovative features to streamline workflow processes, enhance collaboration, and boost overall productivity in engineering teams. It includes engineering metrics that can help you take action with in-depth insights.

Understanding Engineering Productivity Metrics

Below are a few important engineering metrics that can help in measuring their productivity:

Merge Frequency

Merge Frequency represents the rate at which the Pull Requests are merged into any of the code branches per day. Engineering teams can optimize their development workflows, improve collaboration, and increase team efficiency.

‍

Cycle Time

Cycle time measures the time it takes to complete a single iteration of a process or task. Organizations can identify opportunities for process optimization and efficiency improvement by tracking cycle time.

‍

Deployment PR

Deployment PRs represent the average number of Pull Requests merged in the main/master/production branch per week. Measuring it helps improve Engineering teams’ efficiency by providing insights into code deployments’ frequency, timing, and success rate.

‍

Planning Accuracy

Planning Accuracy represents the percentage of Tasks Planned versus Tasks Completed within a given time frame. Its benchmarks help engineering teams measure their performance, identify improvement opportunities, and drive continuous enhancement of their planning processes and outcomes.

‍

Code Coverage

Code coverage is a measure that indicates the percentage of a codebase that is tested by automated tests. It helps ensure that the tests cover a significant portion of the code, identifying code quality, untested parts, and potential bugs.

‍

How does Typo Help in Enhancing Engineering Productivity?

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.

Features

• Offers customized DORA metrics and other engineering metrics that can be configured in a single dashboard.

• Includes effective sprint analysis feature that tracks and analyzes the team’s progress throughout a sprint.

• Provides 360 views of the developer experience i.e. captures qualitative insights and provides an in-depth view of the real issues.

• Offers engineering benchmark to compare the team’s results across industries.

• User-friendly interface.

Improve Engineering Productivity Always to Stay Ahead

Measuring and improving engineering productivity is essential for achieving project success and driving business growth. By understanding the importance of productivity tracking, leveraging relevant metrics, and implementing effective strategies, organizations can optimize productivity, enhance product quality, and deliver exceptional results in today’s competitive software engineering landscape.

In conclusion, engineering productivity is not just a metric; it’s a mindset and a continuous journey towards excellence.

A software development team is critical for business performance. They wear multiple hats to complete the work and deliver high-quality software to end-users. On the other hand, organizations need to take care of their well-being and measure developer experience to create a positive workplace for them.

Otherwise, this can negatively impact developers’ productivity and morale which makes their work less efficient and effective. As a result, disrupting the developer experience at the workplace.

With Typo, you can capture qualitative insights and get a 360 view of your developer experience. Let’s delve deeper into it in this blog post:

What is Developer Experience?

Developer experience refers to the overall experience of developer teams when using tools, platforms, and services to build software applications. This means right from the documentation to coding and deployment and includes tangible and intangible experience.

Happy developers = positive developer experience. It increases their productivity and morale. It further leads to a faster development cycle, developer workflow, methods, and working conditions.

Not taking care of developer experience can make it difficult for businesses to retain and attract top talent.

Why is Developer Experience Beneficial?

Developer experience isn’t just a buzzword. It is a crucial aspect of your team’s productivity and satisfaction.

Below are a few benefits of developer experience:

Smooth Onboarding Process

Good devex ensures the onboarding process is as simple and smooth as possible. It includes making engineering teams familiar with the tools and culture and giving them the support they need to proceed further in their career. It also allows them to know other developers which can help them in collaboration and mentorship.

Improves Product Quality

A positive developer experience leads to 3 effective C’s – Collaboration, communication, and coordination. Adhering to coding standards, best practices and automated testing also helps in promoting code quality and consistency and catching and fixing issues early. As a result, they can easily create products that meet customer needs and are free from errors and glitches.  

Increases Development Speed

When developer experience is handled carefully, team members can work more smoothly and meet milestones efficiently. Access to well-defined tools, clear documents, streamlined workflow, and a well-configured development environment are a few of the ways to boost development speed. It lets them minimize the need to switch between different tools and platforms which increases the focus and team productivity.

Attracts and Retains Top Talents

Developers usually look out for a strong tech culture so they can focus on their core skills and get acknowledged for their contributions. A good developer experience results in developer satisfaction and aligns their values and goals with the organization. In return, developers bring the best to the table and want to stay in the organization for the long run.

Enhances Collaboration

Great developer experience encourages collaboration and effective communication tools. This fosters teamwork and reduces misunderstandings. Through collaborative approaches, developers can easily discuss issues, share feedback, and work together on tasks.

How to Measure Developer Experience with Typo?

Typo helps with early indicators of their well-being and actionable insights on the areas that need attention through signals from work patterns and continuous AI-driven pulse check-ins on the experience of the developers.

Below is the process that Typo follows to gain insights into developer experience effectively:

Step 1: Pulse Surveys

Pulse surveys refer to short, periodic questionaries used to gather feedback from developers to assess their engagement, satisfaction, and overall organizational health.

Typo’s pulse surveys are specifically designed for the software engineering team as it is built on a developer experience framework. It triggers AI-driven pulse surveys where each developer receives a notification periodically with a few conversational questions.

We highly recommend doing surveys once a month as to keep a tab on your team’s wellbeing & experiences and build a continuous loop of feedback. However, you can customize the frequency of these surveys according to the company’s suitability and needs.

And don’t worry, these surveys are anonymous.

‍

‍

Step 2: Developer Experience Analytics

Based on the responses to the pulse surveys over time, insights are published on the Typo dashboard. These insights help to analyze how developers feel at the workplace, what needs immediate attention, how many developers are at risk of burnout and much more.

Below are key components of Typo’s developer experience analytics dashboard:

DevEx Score

The DevEx score indicates the overall state of well-being or happiness within an organization. It reflects the collective emotional and mental health of the developers.

Also known as the employee net promoter score, this score ranges between 1 – 10 as shown in the image below. It is based on the developer feedback collected. A high well-being score suggests that people are generally content and satisfied while a low score may indicate areas of concern or areas needing improvement.

Response Rate

It is the percentage of people who responded to the check-in. A higher response rate represents a more reliable dataset for analyzing developer experience metrics and deriving insights.

This is a percentage number along with the delta change. You will also see the exact count to drive this percentage.  It also includes the trend graph showing the data from the last 4 weeks.

It also includes trending sentiments that show you the segregation of employees based on the maximum re-occurring sentiments as mentioned by developer team.

‍

‍Recent comments

This section shows all the concerns raised by developers which you can reply to and drive meaningful conversations. This offers valuable insights into their workflow challenges, addresses issues promptly, and boosts developer satisfaction.

‍

Heatmap

In this section, you can slice and dice your data to deep-dive further on the level of different demographics. The list of demo graphies is as follows:

• Designation

• Location

• Team

• Tenure

Burnout Alerts

Typo sends automated alerts to your communication to help you identify burnout signs in developers at an early stage. This enables leaders to track developer engagement and support their well-being, maintain productivity, and create a positive and thriving work environment.

Typo tracks the work habits of developers across multiple activities, such as commits, PRs, reviews, comments, tasks, and merges, over a certain period. If these patterns consistently exceed the average of other developers or violate predefined benchmarks, the system identifies them as being in the burnout zone or at risk of burnout. These benchmarks can be customized to meet your specific needs.

Developer Experience Framework, Powered by Typo

Typo’s developer experience framework suggests to engineering leaders what they should focus on for measuring the dev productivity and experience.  

Below are the key focus areas and their drivers incorporated in the developer experience framework:

Key Focus Areas

Manager Support

It refers to the level of assistance, guidance, and resources provided by managers or team leads to support developers in their work.

Developer Flow

It is a state of optimal engagement and productivity that developers experience when fully immersed and focused on their work.

‍Product Management

The practices involved overseeing a software product’s lifecycle, from ideation to development, launch, and ongoing management.

Development Releases

It refers to creating and deploying software solutions or updates, emphasizing collaboration, streamlined workflows, and reliable deployment to enhance the developer experience.

Culture and Values

It includes shared beliefs, norms, and principles that shape a positive work environment. It includes collaboration, open communication, respect, innovation, diversity, and inclusion, fostering creativity, productivity, and satisfaction among developers.

Conclusion

Measuring developer experience continuously is crucial in today’s times. It helps to provide real-time feedback on workflow efficiency, early signs of burnout, and overall satisfaction levels. This further identifies areas for improvement and fosters a more productive and enjoyable work environment for developers.

To learn more about DevEx, visit our website!

In today’s times, developer experience has become an integral part of any software development company. A direct relationship exists between developer experience and developer productivity. A positive developer experience leads to high developer productivity, increasing job satisfaction, efficiency, and high-quality products.

When organizations don’t focus on developer experience, they may encounter many problems in workflow. This negatively impacts the overall business performance.

In this blog, let’s learn more about the developer experience framework that is beneficial to developers, engineering managers, and organizations.

What is Developer Experience?

In simple words, Developer experience is about the experience software developers have while working in the organization.

It is the developers’ journey while working with a specific framework, programming languages, platform, documentation, general tools, and open-source solutions.

Positive developer experience = Happier teams

Developer experience has a direct relationship with developer productivity. A positive experience results in high dev productivity which further leads to high job satisfaction, performance, and morale. Hence, happier developer teams.

This starts with understanding the unique needs of developers and fostering a positive work culture for them.

Benefits of Developer Experience

Smooth Onboarding Process

DX ensures that the onboarding process is simple and smooth as possible. This includes making them familiar with the tools and culture as well as giving them the support they need to proceed further in their career.

It also allows them to know other developers which help in collaboration, open communication, and seeking help, whenever required.

Improves Product Quality

A positive developer experience leads to 3 effective C’s - Collaboration, communication, and coordination. Besides this, adhering to coding standards, best practices, and automated testing helps in promoting code quality and consistency and catching and fixing issues early.

As a result, they can easily create products that can meet customer needs and are free from errors and glitches.  

Increases Development Speed

When developer experience is handled with care, software developers can work more smoothly and meet milestones efficiently. Access to well-defined tools, clear documents, streamlined workflow, and a well-configured development environment are a few of the ways to boost development speed.

It also lets them minimize the need to switch between different tools and platforms which increases the focus and team productivity.

Attract and Retain Top Talents

Developers usually look out for a strong tech culture. So they can focus on their core skills and get acknowledged for their contributions. A good developer experience increases job satisfaction and aligns their values and goals with the organization.

In return, developers bring the best to the table and want to stay in the organization for the long run.

Enhanced Collaboration

The right kind of developer experience encourages collaboration and effective communication tools. This fosters teamwork and reduces misunderstandings.

Through collaborative approaches, developers can easily discuss issues, share feedback, and work together on tasks. It helps streamline the development process and results in high-quality work.

Two Key Frameworks and Their Limitations

There are two frameworks to measure developer productivity. However, they come with certain drawbacks. Hence, a new developer framework is required to bridge the gap in how organizations approach developer experience and productivity.

Let’s take a look at DORA metrics and SPACE frameworks along with their limitations:

DORA Metrics

DORA metrics have been identified after 6 years of research and surveys by DORA. It assists engineering leaders to determine two things:

• The characteristics of a top-performing team

• How their performance compares to the rest of the industry

It defines 4 key metrics:

Deployment frequency

Deployment Frequency measures the frequency of deployment of code to production or releases to end-users in a given time frame.

Lead Time for Changes

Also known as cycle time. Lead Time for Changes measures the time between a commit being made and that commit making it to production.

Mean Time to Recover

This metric is also known as the mean time to restore. Mean Time to Recover measures the time required to solve the incident i.e. service incident or defect impacting end-users.

Change Failure Rate

Change Failure Rate measures the proportion of deployment to production that results in degraded services.

‍

‍

Limitations of DORA metrics

It Doesn't Take into Consideration All the Factors that Add to the Success of the Development Process

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.

It Doesn't Provide Full Context

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 Constantly Evolving

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.

SPACE Framework

This framework helps in understanding and measuring developer productivity. It takes into consideration both the qualitative and quantitative aspects and uses various data points to gauge the team's productivity.

The 5 dimensions of this framework are:

Satisfaction and Well-Being

The dimension of developers’ satisfaction and well-being is often evaluated through developer surveys, which assess whether team members are content, happy, and exhibiting healthy work practices. There is a strong connection between contentment, well-being, and productivity, and teams that are highly productive but dissatisfied are at risk of burning out if their well-being is not improved.

Performance

The SPACE Framework originators recommend evaluating a developer’s performance based on their work outcome, using metrics like Defect Rate and Change Failure Rate. Every failure in production takes away time from developing new features and ultimately harms customers.

Activity

The Velocity framework includes activity metrics that provide insights into developer outputs, such as on-call participation, pull requests opened, the volume of code reviewed, or documents written, which are similar to older productivity measures. However, the framework emphasizes that such activity metrics should not be viewed in isolation but should be considered in conjunction with other metrics and qualitative information.

Communication and Collaboration:

Teams that are highly transparent and communicative tend to be the most successful. This enables developers to have a clear understanding of their priorities, and how their work contributes to larger projects, and also facilitates knowledge sharing among team members.

Indicators that can be used to measure collaboration and communication may include the extent of code review coverage and the quality of documentation.

Efficiency and Flow

The concept of efficiency in the SPACE framework pertains to an individual’s ability to complete tasks quickly with minimal disruption, while team efficiency refers to the ability of a group to work effectively together. These are essential factors in reducing developer frustration.

‍

‍

Limitations of SPACE framework

It Doesn’t Tell You WHY

While the SPACE framework measures dev productivity, it doesn’t tell why certain measurements have a specific value nor can tell the events that triggered a change. This framework offers a structured approach to evaluating internal and external factors but doesn’t delve into the deeper motivations driving these factors.

Limited Scope for Innovation

Too much focus on efficiency and stability can stifle developers’ creativity and innovation. The framework can make teams focus more on hitting specific targets. A culture that embraces change, experiments, and a certain level of uncertainty doesn’t align with the framework principles.

Too Many Metrics

This framework has 5 different dimensions and multiple metrics. Hence, it produces an overwhelming amount of data. Further, engineering leaders need to set up data, maintain data accuracy, and analyze these results. This makes it difficult to identify critical insights and prioritize actions.

Need for a new Developer Experience Framework

This new framework suggests to organizations and engineering leaders what they should focus on for measuring the dev productivity and experience.  

Below are the key focus areas and their drivers incorporated in the Developer Experience Framework:

Manager Support

Refers to the level of assistance, guidance, and resources provided by managers or team leads to support developers in their work.

Empathy

The ability to understand and relate to developers, actively listen, and show compassion in interactions.

Coach and Guide

The role of managers is to provide expertise, advice, and support to help developers improve their skills, overcome challenges, and achieve career goals.

Feedback

The ability to provide timely and constructive feedback on performance, skills, and growth areas helping developers gain insights, refine their skills, and work towards achieving their career objectives.

Developer flow

Refers to a state of optimal engagement and productivity that developers experience when they are fully immersed and focused on their work.

Work-Life Balance

Maintaining a healthy equilibrium between work responsibilities and personal life promotes well-being, boundaries, and resources for managing workload effectively.

Autonomy

Providing developers with the freedom and independence to make decisions, set goals, and determine their approach and execution of tasks.

Focus Time

The dedicated periods of uninterrupted work where developers can deeply concentrate on their tasks without distractions or interruptions.

Goals

Setting clear objectives that provide direction, motivation, and a sense of purpose in developers' work, enhances their overall experience and productivity.

Product Management

Refers to the practices involved in overseeing the lifecycle of a software product, from ideation to development, launch, and ongoing management.

Clear Requirements

Providing developers with precise and unambiguous specifications, ensuring clarity, reducing ambiguity, and enabling them to meet the expectations of stakeholders and end-users.

Reasonable Timelines

Setting achievable and realistic project deadlines, allowing developers ample time to complete tasks without undue pressure or unrealistic expectations.

Collaborative Discussions

Fostering open communication among developers, product managers, and stakeholders, enabling constructive discussions to align product strategies, share ideas, and resolve issues.

Development and Releases

Refers to creating and deploying software solutions or updates, emphasizing collaboration, streamlined workflows, and reliable deployment to enhance the developer experience.

Tools and Technology

Providing developers with the necessary software tools, frameworks, and technologies to facilitate their work in creating and deploying software solutions.

Code Health

Involves activities like code refactoring, performance optimization, and enforcing best practices to ensure code quality, maintainability, and efficiency, thereby enhancing the developer experience and software longevity.

Frictionless Releases

Streamlining software deployment through automation, standardized procedures, and effective coordination, reducing errors and delays for a seamless and efficient process that enhances the developer experience.

Culture and Values

Refers to shared beliefs, norms, and principles that shape a positive work environment. It includes collaboration, open communication, respect, innovation, diversity, and inclusion, fostering creativity, productivity, and satisfaction among developers.

Psychological Safety

Creating an environment where developers feel safe to express their opinions, take risks, and share their ideas without fear of judgment or negative consequences.

Recognition

Acknowledging and appreciating developers' contributions and achievements through meaningful recognition, fostering a positive and motivating environment that boosts morale and engagement.

Team Collaboration

Fostering open communication, trust, and knowledge sharing among developers, enabling seamless collaboration, and idea exchange, and leveraging strengths to achieve common goals.

Learning and Growth

Continuous learning and professional development, offering skill-enhancing opportunities, encouraging a growth mindset, fostering curiosity and innovation, and supporting career progression.

Conclusion

The developer experience framework creates an indispensable link between developer experience and productivity. Organizations that neglect developer experience face workflow challenges that can harm business performance.  

Prioritizing developer experience isn’t just about efficiency. It includes creating a work culture that values individual developers, fosters innovation, and propels software development teams toward unparalleled success.

Typo aligns seamlessly with the principles of the Developer Experience Framework, empowering engineering leaders to revolutionize their teams.

Happy developers are more engaged and productive in the organization. They are more creative and less likely to quit.

But, does developer happiness only come from the fair compensation provided to them? While it is one of the key factors, other aspects also contribute to their happiness.

As the times are changing, there has been a shift in developers’ perspective too. From ‘What we do for a Living’, they now believe in ‘How we want to live’. Happiness is now becoming a major driving force in their decision whether to take, stay, or leave a job.

In this blog, let’s delve deeper into developer happiness and ways to improve it in the organization:

What is Developer Happiness?

In simple words, Developer happiness can be defined as a ‘State of having a positive attitude and outlook on one’s work’.

It is one of the essential elements of organizational success. An increase in developer happiness results in higher engagement and job satisfaction. This gives software developers the freedom to be human and survive and thrive in the organization.

‍

Below are a few benefits of having happy developers in the workplace:

Breed Innovation

Happy developers have a positive mindset toward their jobs and organization. They are most likely to experiment with new ideas and contribute to creative solutions. They are more likely to take calculated risks and step out of their comfort zone to foster innovation and try new approaches.

Faster Problem Resolution

Having a positive mindset leads to quicker problem-solving. When software developers are content, they are open to collaboration with other developers and increase communication. This facilitates faster issue resolution, anticipates potential issues, and addresses them before they escalate.

Ownership and Accountability

Developer happiness comes from a positive work environment. When they feel valued and happy about their work, they take responsibility for resolving issues promptly and align their work and the company goals. They become accountable for their work and want to give their best. This not only increases their work satisfaction but developer experience as well.

Improves Code Quality

Happy developers are more likely to pay attention to the details of their code. They ensure that the work is clean and adheres to the best practices. They are more open to and cooperative during code reviews and take feedback as a way to improve and not criticism.

Health and Well-Being

A positive work environment, supportive team, and job satisfaction result in developer happiness. This reduces their stress and burnout and hence, improves developers’ overall mental and physical well-being.

The above-mentioned points also result in increased developer productivity. In the next section, let’s understand how developer happiness is related to developer productivity.

How Developer Happiness and Developer Productivity are Related to Each Other?

According to the Social Market Foundation, Happy employees are 12% more productive than unhappy employees on average.

Developer Happiness is closely linked to Developer Productivity.

Happy developers perform their tasks well. They treat their customers well and take their queries seriously. This results in happy customers as well. These developers are also likely to take fewer sick leaves and work breaks. Hence, showcasing their organization and its work culture in a good picture.

Moreover, software developers find fulfillment in their roles. This increases their enthusiasm and commitment to their work. They wouldn’t mind going the extra mile to achieve project goals and perform their tasks well.

As a result, happy developers are highly motivated and engaged in their work which leads to increased productivity and developer experience.

Three Core Aspects of Developer Happiness

Following are the three pillars of developer happiness:

Right Tools and Technologies

Tools have a huge impact on developer happiness and retention. The latest and most reliable tools save a lot of time and effort. It makes them more effective in their roles and improves their day-to-day tasks. This helps in creating the flow state, comfortable cognitive leads, and feedback loops.

Passionate Developer Teams

When developers have more control over their roadmaps, it challenges them intellectually and allows them to make meaningful decisions. Having autonomy and a sense of ownership over their work, allows them to deliver efficient and high-quality software products.

Positive Engineering Culture

The right engineering culture creates space for developers to learn, experiment, and share. It allows them to have an ownership mindset, encourages strong agile practices, and is a foundation for productive and efficient teams that drive the business forward. A positive engineering culture also prioritizes psychological safety.

Ways to Boost Developer Happiness in the Workplace

Use Relevant Tools and Technologies

One of the main ways to improve developer happiness is to invest in the right tools and technologies. Experiment with these tools from time to time and monitor the progress of it. If it seems to be the right fit, go ahead with them. However, be cautious to not include every latest tool and technology that comes your way. Use those that you seem are relevant, updated, and compatible with the software. You can also set policies for how someone can obtain new equipment.

The combination of efficient workspace and modern learning tools helps in getting better work from developers. This also increases their productivity and hence, results in developer happiness.

Flexible Work Arrangement

When developers have control over their working style and work schedules, it gives them a healthy work-life balance. As in changing times, we all are changing our perspective not everyone is meant for 9-5 jobs. The flexibility allows developers to when their productivity is at its peak. This becomes a win-win situation for developer satisfaction and project success.

For team communication and collaboration, particular core hours can be set. I.e. 12 PM - 5 PM. After that, anyone can work at any time of the day. Apart from this, asynchronous communication can also be encouraged to accommodate varied work schedules.

Ensure that there are open communication channels to understand the evolving needs and preferences of the development team.

Keep Realistic Expectations

Ensure that you don’t get caught up in completing objectives. Understand that you are dealing with human beings. Hence, keep realistic expectations and deadlines from your developers. Know that good software takes time. It includes a lot of planning, effort, energy, and commitment to create meaningful projects. Also, consider their time beyond work as well. By taking note of all of these, set expectations accordingly.

But that’s not all! Ensure that you prioritize quality over quantity. This not only boosts their confidence in skills and abilities but also allows them to be productive and satisfied with their role.

‍

‍

Enable Deep Focus

Software development job is demanding and requires a lot of undivided focus. Too many meetings or overwork can distract them and lose their focus. The state of flow is important for deep work. If a developer is working from the office, having booths can be helpful. They can isolate themselves and focus deeply on work. If they are working remotely, developers can turn off notifications or set the status as focused time.

Focus time can be as long as two hours to less than half an hour. Make sure that they are taking breaks between these focus sessions. You can make them aware of time management techniques. So, that they know how to manage their time effectively and efficiently.

Promote Continuous Learning

Software development is an ever-changing field. Hence, developers need to upskill and stay up to date with the latest developments. Have cross-sharing and recorded training sessions so that they are aware of the current trends in the software development industry. You can also provide them with the necessary courses, books, and newsletters.

Apart from this, you can also do task-shifting so they don’t feel their work to be monotonous. Give them time and space to skill up before any new project starts.

Appreciation and Recognition

Developers want to know their work counts and feel proud of their job. They want to be seen, valued, heard and understood. To foster a positive work environment, celebrate their achievements. Even a ‘Thankyou’ goes a long way.

Since developers' jobs are demanding, they have a strong emotional need to be recognized for their accomplishments. They expect genuine appreciation and recognition that match the impact or output. It should be publicly acknowledged.

You can give them credit for their work in daily or weekly group meetings. This increases their job satisfaction, retention, and productivity.

Improves Overall Well-Being

The above-mentioned points help developers improve their physical and mental well-being. It not only helps them in the work front but also their personal lives. When developers aren’t loaded with lots of work, it lets them be more creative in solving problems and decision-making. It also encourages a healthy lifestyle and allows them to have proper sleep.

You can also share mental health resources and therapists' details with your developers. Besides this, you can have seminars and workshops on how health is important and promote physical activities such as walking, playing outdoor games, swimming, and so on.

Conclusion

Fostering developer happiness is not just a desirable goal, but rather a driving force for an organization’s success. By investing in supportive cultures, effective tools, and learning opportunities, Organizations can empower developers to perform their development tasks well and unleash their full potential.

Typo helps in revolutionizing your team's efficiency and happiness.

The software development field is constantly evolving field. While this helps deliver the products and services quickly to the end-users, it also implies that developers might take shortcuts to deliver them on time. This not only reduces the quality of the software but also leads to increased technical debt.

But, with new trends and technologies, comes generative AI. It seems to be a promising solution in the software development industry which can ultimately, lead to high-quality code and decreased technical debt.

Let’s explore more about how generative AI can help manage technical debt!

Technical debt: An overview

Technical debt arises when development teams take shortcuts to develop projects. While this gives them short-term gains, it increases their workload in the long run.

In other words, developers prioritize quick solutions over effective solutions. The four main causes behind technical debt are:

• Business causes: Prioritizing business needs and the company’s evolving conditions can put pressure on development teams to cut corners. It can result in preponing deadlines or reducing costs to achieve desired goals.
• Development causes: As new technologies are evolving rapidly, It makes it difficult for teams to switch or upgrade them quickly. Especially when already dealing with the burden of bad code.
• Human resources causes: Unintentional technical debt can occur when development teams lack the necessary skills or knowledge to implement best practices. It can result in more errors and insufficient solutions.
• Resources causes: When teams don’t have time or sufficient resources, they take shortcuts by choosing the quickest solution. It can be due to budgetary constraints, insufficient processes and culture, deadlines, and so on.

Why generative AI for code management is important?

As per McKinsey’s study,

“… 10 to 20 percent of the technology budget dedicated to new products is diverted to resolving issues related to tech debt. More troubling still, CIOs estimated that tech debt amounts to 20 to 40 percent of the value of their entire technology estate before depreciation.”

But there’s a solution to it. Handling tech debt is possible and can have a significant impact:

“Some companies find that actively managing their tech debt frees up engineers to spend up to 50 percent more of their time on work that supports business goals. The CIO of a leading cloud provider told us, ‘By reinventing our debt management, we went from 75 percent of engineer time paying the [tech debt] ‘tax’ to 25 percent. It allowed us to be who we are today.”

There are many traditional ways to minimize technical debt which includes manual testing, refactoring, and code review. However, these manual tasks take a lot of time and effort. Due to the ever-evolving nature of the software industry, these are often overlooked and delayed.

Since generative AI tools are on the rise, they are considered to be the right way for code management which subsequently, lowers technical debt. These new tools have started reaching the market already. They are integrated into the software development environments, gather and process the data across the organization in real-time, and further, leveraged to lower tech debt.

Some of the key benefits of generative AI are:

• Identify redundant code: Generative AI tools like Codeclone analyze code and suggest improvements. This further helps in improving code readability and maintainability and subsequently, minimizing technical debt.
• Generates high-quality code: Automated code review tools such as Typo help in an efficient and effective code review process. They understand the context of the code and accurately fix issues which leads to high-quality code.  
• Automate manual tasks: Tools like Github Copilot automate repetitive tasks and let the developers focus on high-quality tasks.
• Optimal refactoring strategies: AI tools like Deepcode leverage machine learning models to understand code semantics, break it down into more manageable functions, and improve variable namings.

Case studies and real-life examples

Many industries have started adopting generative AI technologies already for tech debt management. These AI tools assist developers in improving code quality, streamlining SDLC processes, and cost savings.

Below are success stories of a few well-known organizations that have implemented these tools in their organizations:

Microsoft uses Diffblue cover for Automated Testing and Bug Detection

Microsoft is a global technology leader that implemented Diffblue cover for automated testing. Through this generative AI, Microsoft has experienced a considerable reduction in the number of bugs during the development process. It also ensures that the new features don’t compromise with existing functionality which positively impacts their code quality. This further helps in faster and more reliable releases and cost savings.

Google implements Codex for code documentation

Google is an internet search engine and technology giant that implemented OpenAI’s Codex for streamlining code documentation processes. Integrating this AI tool helped subsequently reduce the time and effort spent on manual documentation tasks. Due to the consistency across the entire codebase, It enhances code quality and allows developers to focus more on core tasks.

Facebook adopts CodeClone to identify redundancy

Facebook, a leading social media, has adopted a generative AI tool, CodeClone for identifying and eliminating redundant code across its extensive codebase. This resulted in decreased inconsistencies and a more streamlined and efficient codebase which further led to faster development cycles.

Pioneer Square Labs uses GPT-4 for higher-level planning

Pioneer Square Labs, a studio that launches technology startups, adopted GPT-4 to allow developers to focus on core tasks and let these AI tools handle mundane tasks. This further allows them to take care of high-level planning and assist in writing code. Hence, streamlining the development process.

How Typo leverage generative AI to reduce technical debt?

Typo’s automated code review tool enables developers to merge clean, secure, high-quality code, faster. It lets developers catch issues related to maintainability, readability, and potential bugs and can detect code smells.

Typo also auto-analyses your codebase pulls requests to find issues and auto-generates fixes before you merge to master. Its Auto-Fix feature leverages GPT 3.5 Pro trained on millions of open source data & exclusive anonymised private data as well to generate line-by-line code snippets where the issue is detected in the codebase.

As a result, Typo helps reduce technical debt by detecting and addressing issues early in the development process, preventing the introduction of new debt, and allowing developers to focus on high-quality tasks.

Issue detection by Typo

Autofixing the codebase with an option to directly create a Pull Request

Key features

Supports top 10+ languages

Typo supports a variety of programming languages, including popular ones like C++, JS, Python, and Ruby, ensuring ease of use for developers working across diverse projects.

Fix every code issue

Typo understands the context of your code and quickly finds and fixes any issues accurately. Hence, empowering developers to work on software projects seamlessly and efficiently.

Efficient code optimization

Typo uses optimized practices and built-in methods spanning multiple languages. Hence, reducing code complexity and ensuring thorough quality assurance throughout the development process.

Professional coding standards

Typo standardizes code and reduces the risk of a security breach.

Click here to know more about our Code Review tool

Can technical debt increase due to generative AI?

While generative AI can help reduce technical debt by analyzing code quality, removing redundant code, and automating the code review process, many engineering leaders believe technical debt can be increased too.

Bob Quillin, vFunction chief ecosystem officer stated “These new applications and capabilities will require many new MLOps processes and tools that could overwhelm any existing, already overloaded DevOps team,”

They aren’t wrong either!

Technical debt can be increased when the organizations aren’t properly documenting and training development teams in implementing generative AI the right way. When these AI tools are adopted hastily without considering any long-term implications, they can rather increase the workload of developers and increase technical debt.

Ethical guidelines

Establish ethical guidelines for the use of generative AI in organizations. Understand the potential ethical implications of using AI to generate code, such as the impact on job displacement, intellectual property rights, and biases in AI-generated output.

Diverse training data quality

Ensure the quality and diversity of training data used to train generative AI models. When training data is biased or incomplete, these AI tools can produce biased or incorrect output. Regularly review and update training data to improve the accuracy and reliability of AI-generated code.

Human oversight

Maintain human oversight throughout the generative AI process. While AI can generate code snippets and provide suggestions, the final decision should be upon the developers for final decision making, review, and validate the output to ensure correctness, security, and adherence to coding standards.

Most importantly, human intervention is a must when using these tools. After all, it’s their judgment, creativity, and domain knowledge that help to make the final decision. Generative AI is indeed helpful to reduce the manual tasks of the developers, however, they need to use it properly.

Conclusion

In a nutshell, generative artificial intelligence tools can help manage technical debt when used correctly. These tools help to identify redundancy in code, improve readability and maintainability, and generate high-quality code.

However, it is to be noted that these AI tools shouldn’t be used independently. These tools must work only as the developers’ assistants and they muse use them transparently and fairly.

The code review process is one of the major reasons for developer burnout. This not only hinders the developer’s productivity but also negatively affects the software tasks. Unfortunately, it is a crucial aspect of software development that shouldn’t be compromised.

So, what is the alternative to manual code review? Let’s dive in further to know more about it:

The current State of Manual Code Review

Manual code reviews are crucial for the software development process. It can help identify bugs, mentor new developers, and promote a collaborative culture among team members. However, it comes with its own set of limitations.

Software development is a demanding job with lots of projects and processes. Code review when done manually, can take a lot of time and effort from developers. Especially, when reviewing an extensive codebase. It not only prevents them from working on other core tasks but also leads to fatigue and burnout, resulting in decreased productivity.

Since the reviewers have to read the source code line by line to identify issues and vulnerabilities, it can overwhelm them and they may miss out on some of the critical paths. This can result in human errors especially when the deadline is approaching. Hence, negatively impacting project efficiency and straining team resources.

In short, manual code review demands significant time, effort, and coordination from the development team.

This is when AI code review comes to the rescue. AI code review tools are becoming increasingly popular in today’s times. Let’s read more about AI code review and why is it important for developers:

What is AI Code Review?

AI code review is an automated process that examines and analyzes the code of software applications. It uses artificial intelligence and machine learning techniques to identify patterns, detect potential problems, common programming mistakes, and potential security vulnerabilities. These AI code review tools are entirely based on data so they aren’t biased and can read vast amounts of code in seconds.

Why AI in the Code Review Process is Important?

Augmenting human efforts with AI code review has various benefits:

Enhance Overall Quality

Generative AI in code review tools can detect issues like potential bugs, security vulnerabilities, code smells, bottlenecks, and more. The human code review process usually overlooks these issues. Hence, helping in identifying patterns and recommending code improvements that can enhance efficiency and maintenance and reduce technical debt. This leads to robust and reliable software that meets the highest quality standards.

Improve Productivity

AI-powered tools can scan and analyze large volumes of code within minutes. It not only detects potential issues but also suggests improvements according to coding standards and practices. This allows the development team to catch errors early in the development cycle by providing immediate feedback. This saves time spent on manual inspections and rather, developers can focus on other intricate and imaginative parts of their work.

Better Compliance with Coding Standards

The automated code review process ensures that code conforms to coding standards and best practices. It allows code to be more readable, understandable, and maintainable. Hence, improving the code quality. Moreover, it enhances teamwork and collaboration among developers as all of them adhere to the same guidelines and consistency in the code review process.

Enhance Accuracy

The major disadvantage of manual code reviews is that they are prone to human errors and biases. It further increases other critical issues related to structural quality, architectural decisions or so which negatively impact the software application. Generative AI in code reviews can analyze code much faster and more consistently than humans. Hence, maintaining accuracy and reducing biases since they are entirely based on data.

Increase Scalability

When software projects grow in complexity and size, manual code reviews become increasingly time-consuming. It may also struggle to keep up with the scale of these codebases which further delay the code review process. As mentioned before, AI code review tools can handle large codebases in a fraction of a second and can help development teams maintain high standards of code quality and maintainability.  

How Typo Leverage Gen AI to Automate Code Reviews?

Typo’s automated code review tool not only enables developers to merge clean, secure, high-quality code, faster. It lets developers catch issues related to maintainability, readability, and potential bugs and can detect code smells. It auto-analyses your codebase and pulls requests to find issues and auto-generates fixes before you merge to master.

Typo’s Auto-Fix feature leverages GPT 3.5 Pro to generate line-by-line code snippets where the issue is detected in the codebase. This means less time reviewing and more time for important tasks. As a result, making the whole process faster and smoother.

Issue detection by Typo

‍

‍

Auto fixing the codebase with an option to directly create a Pull Request

‍

‍

Key Features

Supports Top 10+ Languages

Typo supports a variety of programming languages, including popular ones like C++, JS, Python, and Ruby, ensuring ease of use for developers working across diverse projects.

Fix Every Code Issue

Typo understands the context of your code and quickly finds and fixes any issues accurately. Hence, empowering developers to work on software projects seamlessly and efficiently.

Efficient Code Optimization

Typo uses optimized practices and built-in methods spanning multiple languages. Hence, reducing code complexity and ensuring thorough quality assurance throughout the development process.

Professional Coding Standards

Typo standardizes code and reduces the risk of a security breach.

Comparing Typo with Other AI Code Review Tools

There are other popular AI code review tools available in the market. Let’s compare how we stack against others:

AI vs. Humans: The Future of Code Reviews?

AI code review tools are becoming increasingly popular. One question that has been on everyone’s mind is whether these AI code review tools will take away developers’ jobs.

The answer is NO.

Generative AI in code reviews is designed to enhance and streamline the development process. It lets the developers automate the repetitive and time-consuming tasks and focus on other core aspects of software applications. Moreover, human judgment, creativity, and domain knowledge are crucial for software development that AI cannot fully replicate.

While these tools excel at certain tasks like analyzing codebase, identifying code patterns, and software testing, they still cannot fully understand complex business requirements, and user needs, or make subjective decisions.

As a result, the combination of AI code review tools and developers’ intervention is an effective approach to ensure high-quality code.

Conclusion

The tech industry is demanding. The software engineering team needs to stay ahead of the industry trends. New AI tools and technologies can help them complement their skills and expertise and make their task easier.

AI in the code review process offers remarkable benefits including reducing human error and consistent accuracy. But, make sure that they are here to assist you in your task, not your whole strategy or replace you.

Generative AI has become a transformative force in the tech world. And it isn’t going to stop anytime soon! It will continue to have a major impact, especially in the software development industry.Generative AI, when used in the right way, can help developers in saving their time and effort. It allows them to focus on core tasks and upskilling. It further helps streamline various stages of SDLC and improves Developer Productivity. In this article, let’s dive deeper into how generative AI can positively impact developer productivity.

What is Generative AI?

Generative AI is a category of AI models and tools that are designed to create new content, images, videos, text, music, or code. It uses various techniques including neural networks and deep learning algorithms to generate new content.Generative artificial intelligence holds a great advantage for software developers in improving their productivity. It not only improves code quality and delivers better products and services but also allows them to stay ahead of their competitors.Below are a few benefits of Generative AI:

Increases Efficiency

With the help of Generative AI, developers can automate tasks that are either repetitive or don’t require much attention. This saves a lot of time and energy and allows developers to be more productive and efficient in their work. Hence, they can focus on more complex and critical aspects of the software without constantly stressing about other work.

Improves Quality

Generative AI can help in minimizing errors and address potential issues early. When they are set as per the coding standards, it can contribute to more effective coding reviews. This increases the code quality and decreases costly downtime and data loss.

Helps in Learning and Assisting with Work

Generative AI can assist developers by analyzing and generating examples of well-structured code, providing suggestions for refactoring, generating code snippets, and detecting blind spots. This further helps developers in upskilling and gaining knowledge about their tasks.

Cost Savings

Integrating generative AI tools can reduce costs. It enables developers to use existing codebases effectively and complete projects faster even with shorter teams. Generative AI can streamline the stages of the software development life cycle and get the most out of less budget.

Predict Analytics

Generative AI can help in detecting potential issues in the early stages by analyzing historical data. It can also make predictions about future trends. This allows developers to make informed decisions about their projects, streamline their workflow, and hence, deliver high-quality products and services.