The ticking clock of coding time is often considered a factor in making or breaking the success of a development project. When developers manage it well, teams can meet deadlines, deliver high-quality software, and foster collaboration.
However, sometimes coding times are high. This can cause many internal issues and affect the product development cycle.
This blog will address why coding time is high sometimes and how you can improve it.
Coding time is the time it takes from the first commit to a branch to the eventual submission of a pull request. It is a crucial part of the development process where developers write and refine their code based on the project requirements.
High coding time can lead to prolonged development cycles, affecting delivery timelines. Coding time is crucial in the software development lifecycle as it can directly impact the cycle time.
Thus, managing the coding time efficiently to ensure the code completion is done on time with quicker feedback loops and a frictionless development process is essential.
Maintaining the right coding time has several benefits for engineering teams.
When you reduce the coding time, developers can complete more tasks. This moves the project faster and results in shorter development cycles.
With less time spent on coding, developers can have enough time for collaborative activities such as code reviews. These are crucial for a team to function well and enable knowledge sharing.
When coding time is lesser, developers can focus more on quality by conducting testing and debugging processes. This results in cleaner code.
While less coding time has several benefits, this often isn’t the reality. However, high coding time is not just the result of a team member being lazy – several reasons cause high coding time.
Whenever the tasks or features are complicated, additional coding time is needed compared to the more straightforward tasks.
Developers also try to complete the entire tasks in one go which can be hard to achieve. This leads to the developer getting overwhelmed and, eventually, prolonging the coding time. Code review plays a vital role in this context, allowing for constructive feedback and ensuring the quality of the codebase.For software developers, breaking down work into smaller, more manageable chunks is crucial to make progress and stay focused. It’s important to commit small changes frequently to move forward quickly and receive feedback more often. This ensures that the development process runs smoothly and stays on track.
When the requirement is defined poorly, developers will struggle to be efficient. It leads to higher coding time in understanding the requirement, seeking clarification, and making assumptions based on this.
It is essential to establish clear and comprehensive requirements before starting any coding work. This helps developers create an accurate roadmap, pave the way for smoother debugging processes, and reduce the chances of encountering unexpected obstacles. Effective planning and scoping improve the efficiency of the coding process, resulting in timely and satisfactory outcomes.
In a team, there will be developers with different skillset and experience. Additionally, the expertise and familiarity of the developers with the codebase and the technology stack can affect their coding speed.
Maintaining focus and staying on-task while coding is crucial for efficient development. Task languishing is a common issue that can arise due to distractions or shifting priorities, leading to abandoned tasks and decreased productivity.
A survey showed that developers spent only one-third of their time writing new code but spent 35% managing code with code maintenance, testing, and solving security issues.
To avoid this, it’s essential to conduct regular progress reviews. Teams must implement a systematic review process to identify potential issues and address them promptly by reallocating resources as needed. Consistency and focus throughout the development cycle are key for optimizing coding time.
When a developer has too many ongoing projects, they are forced to frequently multitask and switch contexts. This can lead to a reduction in the amount of time they spend working on a particular branch or issue, resulting in an increase in their coding time metric.Use the worklog to understand the dev’s commits over a timeline to different issues. If a developer makes sporadic contributions to various issues, it may be indicative of frequent context switching during a sprint. To mitigate this issue, it is advisable to balance and rebalance the assignment of issues evenly and encourage the team to avoid multitasking by focusing on one task at a time. This approach can help reduce coding time.
Set goals for the work at risk where the rule of thumb is keeping the PR with less than 100 code changes and refactor size as above 50%.To achieve the team goal of reducing coding time, real-time Slack alerts can be utilised to notify the team of work at risk when large and heavily revised PRs are published. By using these alerts, it is possible to identify and address issues, story-points, or branches that are too extensive in scope and require breaking down.
Ensuring fast and efficient code reviews is crucial to optimize coding time. It’s important to inform developers of how timely reviews can speed up the entire development process.
To accomplish this, code review automation tools should be used to improve the review process. These tools can separate small reviews from large ones and automatically assign them to available developers. Furthermore, scheduling specialist reviews can guarantee that complex tasks receive the necessary attention without causing any undue delays.
Improving coding productivity necessitates the adoption of data-driven practices. Teams should incorporate code quality tools that can efficiently monitor coding time and project advancement.
Such tools facilitate the swift identification of areas that require attention, enabling developers to refine their methods continuously. Using data-driven insights is the key to developing more effective coding practices.
Before starting the coding process, thoroughly defining and clarifying the project requirements is extremely important. This crucial step guarantees that developers have a complete comprehension of what needs to be achieved, ultimately resulting in a successful outcome.
Pair programming involves two developers working together on the same code at the same time. This can help reduce coding time by allowing developers to collaborate and share ideas, which can lead to faster problem-solving and more efficient coding. Incorporating the code review process into the pair programming process also ensures the quality of the codebase.
Encouraging open communication and collaboration among team members is crucial to creating a productive and positive work environment. This fosters a culture of teamwork and enables efficient problem-solving through shared ideas. Working together leads to more significant achievements than individuals can accomplish alone.4. Automate Repetitive Processes: Utilize automation tools to streamline repetitive coding tasks, such as code generation or testing, to save time and effort.
Developers must always stay up to date with the latest technologies and best practices. This is crucial for increasing coding speed and efficiency while enhancing the quality of the code. Continuous learning and skill development are essential to maintain a competitive edge in the industry.
To manage workloads and assignments effectively, it is recommended to develop a habit of regularly reviewing the Insights tab, and identifying long PRs on a weekly or even daily basis. Additionally, examining each team member’s workload can provide valuable insights. By using this data collaboratively with the team, it becomes possible to allocate resources more effectively and manage workloads more efficiently.
Using a framework, such as React or Angular, can help reduce coding time by providing pre-built components and libraries that can be easily integrated into the application.
Rapid prototyping involves creating a quick and simple version of the application to test its functionality and usability. This can help reduce coding time by allowing developers to quickly identify and address any issues with the application.
Agile methodologies, such as Scrum and Kanban, emphasize continuous delivery and feedback, which can help reduce coding time by allowing developers to focus on delivering small, incremental improvements to the application.
Reusing code that has already been written can help reduce coding time by eliminating the need to write code from scratch. This can be achieved by using code libraries, modules, and templates.
Incorporating artificial intelligence tools can enhance productivity by automating code review and repetitive tasks, minimizing coding errors, and accelerating the overall development cycle. These AI tools use various techniques including neural networks and machine learning algorithms to generate new content.
Typo provides instantaneous cycle time measurement for both the organization and each development team using their Git provider.
Our methodology divides cycle time into four phases:
When the coding time is high, your main dashboard will display the coding time as red.
Identify delay in the ‘Insights’ section at the team level and sort the teams by the cycle time. Further, click on the team to deep dive into cycle time breakdown of each team and see the delays in the coding time.
Coding times are the cornerstones of efficient software development. Thus, when its impact on project timelines is recognized, engineering teams can imbibe best practices and preventative strategies to deliver quality code on time.
PR cycle time and velocity are two commonly used metrics for measuring the efficiency and effectiveness of software development teams. These metrics help estimate how long your teams can complete a piece of work.
But, among these two, PR cycle time is often prioritized and preferred over velocity.
Therefore, in this blog, we understand the difference between these two metrics. Further, we will dive into the reason behind the PR cycle time over the velocity metric.
PR cycle time measures the process efficiency. In other words, it is the measurement of how much time it takes for your team to complete individual tasks from start to finish. It let them identify bottlenecks in the software development process and implement changes accordingly. Hence, allowing development work to flow smoother and faster through the delivery process.
PR Cycle time lets team members understand how efficiently they are working. A shorter PR cycle time means developers are spending less time waiting for code reviews and integration of code. Hence, indicates a high level of efficiency.
A shorter PR Cycle time means that features or updates can be released to end-users sooner. As a result, it helps them to stay competitive and meet customer demands.
Short PR Cycle time is a key component of agile software development. Hence, allows team members to adapt to changing requirements more easily.
Velocity is the measurement of team efficiency. It estimates how many story points an agile team can complete within a sprint. This metric is usually measured in weeks. As a result, it helps developer teams to plan and decide how much work to include in future sprints. But, the downside is that it doesn’t consider the quality of work or the time it takes to complete individual tasks.
By understanding development velocity, engineering managers and stakeholders can allocate resources more effectively. Hence, it ensures that development teams are neither overburdened or underutilized.
When velocity improves, it gives team members a sense of satisfaction from constantly delivering high-quality products. Hence, it improves their morale and allows them to collaborate with each other effectively.
A decline in velocity metrics signifies potential issues within the development process which includes team conflicts or technical debt. Hence, it allows us to address the issues early to maintain productivity.
PR cycle time is a more objective unit of measurement compared to story points in the production process. While many organizations use story points to estimate time-bound work since it is subjective. Hence, it is easy to manipulate. To increase velocity, you have to overestimate how long it will take to complete the task and therefore, add a larger number to your issue tracker.
Although PR cycle time may also be manipulated, it is most likely to work in your favor. By this, lowering the cycle time allows you to complete the work measurably faster. This further allows you to identify and fix blind spots quickly.
As a result, PR cycle time is a more challenging and tangible goal.
PR cycle time, an essential component of continuous improvement, improves your team’s ability to plan and estimate work. It gives you an accurate picture of how long it will take to move through the development process. Hence, offering real-time visibility and insights into a developer’s task. This further allows you to predict and forecast future work. In case, if the issue goes on longer than expected, you can discuss it with your team on a prior basis.
In the case of velocity, it cannot help in figuring out the why behind the work that took longer than expected. Hence, further planning and predicting the work accordingly wouldn’t be possible in this case.
Outliers are the units of work that take significantly longer than the average. PR cycle time metric is more reliable than the velocity in spotting outliers and anomalies in software development. It is because it measures the time it takes to complete a single unit of work. Therefore, PR cycle time helps in knowing the specific causes of delays in work.
Moreover, it also helps in getting granular insights into the development process. Hence, allowing your engineering team to improve their performance.
Among velocity and PR cycle time metrics, only the latter is directly related to business outcomes. It is a useful metric that determines how fast you can ship value to your customers; allowing you to improve speed and their planning accurately.
Moreover, cycle time is a great metric for continuously improving your team’s ability to iterate quickly. As it can help in spotting bottlenecks, inefficiencies, and areas of improvement in their processes.
Measuring cycle time using Jira or other project management tools is a manual and time-consuming process, which requires reliable data hygiene to deliver accurate results. Unfortunately, most engineering leaders have insufficient visibility and understanding of their teams’ cycle time.
Typo provides instantaneous cycle time measurement for both your organization and each development team using your Git provider.
Our methodology divides cycle time into four phases:
The subsequent phase involves analyzing the various aspects of your cycle time, including the organizational, team, iteration, and even branch levels. For instance, if an iteration has an average review time of 47 hours, you will need to identify the branches that are taking longer than usual and work with your team to address the reasons for the delay.
PR cycle time shouldn’t be the sole metric to measure software development productivity. If you do so, it would mean compromising other aspects of the software development product. Hence, you can balance it with other metrics such as DORA metrics (Deployment frequency, Lead time for change, Change failure rate and Time to restore service) too.
You can familiarize yourself with the SPACE framework when thinking about metrics to adopt in your organization. It is a research-based framework that combines quantitative and qualitative aspects of the developer and the surroundings to give a holistic view of the software development process.
At Typo, we consider the above-mentioned metrics to measure the efficiency and effectiveness of software engineering teams. Through these metrics, you can gain real-time visibility into SDLC metrics, identify bottlenecks and drive continuous improvements.
Imagine having a powerful tool that measures your software team’s efficiency, identifies areas for improvement, and unlocks the secrets to achieving speed and stability in software development – that tool is DORA metrics.
DORA metrics offer valuable insights into the effectiveness and productivity of your team. By implementing these metrics, you can enhance your dev practices and improve outcomes.
In this blog, we will delve into the importance of DORA metrics for your team and explore how they can positively impact your software team’s processes. Join us as we navigate the significance of these metrics and uncover their potential to drive success in your team’s endeavors.
DevOps Research and Assessment (DORA) metrics are a compass for engineering teams striving to optimize their development and operations processes.
In 2015, The DORA team was founded by Gene Kim, Jez Humble, and Dr. Nicole Forsgren to evaluate and improve software development practices. The aim is to enhance the understanding of how development teams can deliver software faster, more reliably, and of higher quality.
Software teams use DORA DevOps metrics in an organization to help improve their efficiency and, as a result, enhance the effectiveness of company deliverables. It is the industry standard for evaluating dev teams and allows them to scale.
The key DORA metrics include deployment frequency, lead time for changes, mean time to recovery, and change failure rate. They have been identified after six years of research and surveys by the DORA team.
To achieve success with DORA metrics, it is crucial to understand them and learn the importance of each metric. Here are the four key DORA metrics:
Implementing DORA Metrics to Improve Dev Performance & Productivity?
Organizations need to prioritize code deployment frequency to achieve success and deliver value to end users. However, it’s worth noting that what constitutes a successful deployment frequency may vary from organization to organization.
Teams that underperform may only deploy monthly or once every few months, whereas high-performing teams deploy more frequently. It’s crucial to continuously develop and improve to ensure faster delivery and consistent feedback. If a team needs to catch up, implementing more automated processes to test and validate new code can help reduce recovery time from errors.
In a dynamic market, agility is paramount. Deployment Frequency measures how frequently code is deployed. Infrequent deployments can cause you to lag behind competitors. Increasing Deployment Frequency facilitates more frequent rollouts, hence, meeting customer demands effectively.
This metric measures the time it takes to implement changes and deploy them to production directly impacts their experience, and this is the lead time for changes.
If we notice longer lead times, which can take weeks, it may indicate that you need to improve the development or deployment pipeline. However, if you can achieve lead times of around 15 minutes, you can be sure of an efficient process. It’s essential to monitor delivery cycles closely and continuously work towards streamlining the process to deliver the best experience for customers.
Picture your software development team tasked with a critical security patch. Measuring Lead Time for Changes helps pinpoint the duration from code commit to deployment. If it goes for a long run, bottlenecks in your CI/CD pipeline or testing processes might surface. Streamlining these areas ensures rapid responses to urgent tasks.
The change failure rate measures the code quality released to production during software deployments. Achieving a lower failure rate than 0-15% for high-performing DevOps teams is a compelling goal that drives continuous improvement in skills and processes. Establishing failure boundaries tailored to your organization’s needs and committing to reducing the failure rate is essential. By doing so, you enhance your software solutions and deliver exceptional user experiences.
Stability is pivotal in software deployment. The change Failure Rate measures the percentage of changes that fail. A high failure rate could signify inadequate testing or insufficient quality control. Enhancing testing protocols, refining code reviews, and ensuring thorough documentation can reduce the failure rate, enhancing overall stability.
Mean Time to Recover (MTTR) measures the time to recover a system or service after an incident or failure in production. It evaluates the efficiency of incident response and recovery processes. Optimizing MTTR aims to minimize downtime by resolving incidents through production changes. The goal is to build robust systems that can detect, diagnose, and rectify problems. Organizations ensure minimal disruption and work towards continuous improvement in incident resolution.
Downtime can be detrimental, impacting revenue and customer trust. MTTR measures the time taken to recover from a failure. A high MTTR indicates inefficiencies in issue identification and resolution. Investing in automation, refining monitoring systems, and bolstering incident response protocols minimizes downtime, ensuring uninterrupted services.
Teams with rapid deployment frequency and short lead time exhibit agile development practices. These efficient processes lead to quick feature releases and bug fixes, ensuring dynamic software development aligned with market demands and ultimately enhancing customer satisfaction.
A short lead time coupled with infrequent deployments signals potential bottlenecks. Identifying these bottlenecks is vital. Streamlining deployment processes in line with development speed is essential for a software development process.
Low comments and minimal deployment failures signify high-quality initial code submissions. This scenario highlights exceptional collaboration and communication within the team, resulting in stable deployments and satisfied end-users.
Teams with numerous comments per PR and a few deployment issues showcase meticulous review processes. Investigating these instances ensures review comments align with deployment stability concerns, ensuring constructive feedback leads to refined code.
Rapid post-review commits and a high deployment frequency reflect agile responsiveness to feedback. This iterative approach, driven by quick feedback incorporation, yields reliable releases, fostering customer trust and satisfaction.
Despite few post-review commits, high deployment frequency signals comprehensive pre-submission feedback integration. Emphasizing thorough code reviews assures stable deployments, showcasing the team’s commitment to quality.
Low deployment failures and a short recovery time exemplify quality deployments and efficient incident response. Robust testing and a prepared incident response strategy minimize downtime, ensuring high-quality releases and exceptional user experiences.
A high failure rate alongside swift recovery signifies a team adept at identifying and rectifying deployment issues promptly. Rapid responses minimize impact, allowing quick recovery and valuable learning from failures, strengthening the team’s resilience.
In collaborative software development, optimizing code collaboration efficiency is paramount. By analyzing Comments per PR (reflecting review depth) alongside Commits after PR is Raised for Review, teams gain crucial insights into their code review processes.
Thorough reviews with limited code revisions post-feedback indicate a need for iterative development. Encouraging developers to iterate fosters a culture of continuous improvement, driving efficiency and learning.
Few comments during reviews paired with significant post-review commits highlight the necessity for robust initial reviews. Proactive engagement during the initial phase reduces revisions later, expediting the development cycle.
The size of pull requests (PRs) profoundly influences deployment timelines. Correlating Large PR Size with Deployment Frequency enables teams to gauge the effect of extensive code changes on release cycles.
Maintaining a high deployment frequency with substantial PRs underscores effective testing and automation. Acknowledge this efficiency while monitoring potential code intricacies, ensuring stability amid complexity.
Infrequent deployments with large PRs might signal challenges in testing or review processes. Dividing large tasks into manageable portions accelerates deployments, addressing potential bottlenecks effectively.
PR size significantly influences code quality and stability. Analyzing Large PR Size alongside Change Failure Rate allows engineering leaders to assess the link between PR complexity and deployment stability.
Frequent deployment failures with extensive PRs indicate the need for rigorous testing and validation. Encourage breaking down large changes into testable units, bolstering stability and confidence in deployments.
A minimal failure rate with substantial PRs signifies robust testing practices. Focus on clear team communication to ensure everyone comprehends the implications of significant code changes, sustaining a stable development environment. Leveraging these correlations empowers engineering teams to make informed, data-driven decisions — a great way to drive business outcomes— optimizing workflows, and boosting overall efficiency. These insights chart a course for continuous improvement, nurturing a culture of collaboration, quality, and agility in software development endeavors.
In the ever-evolving world of software development, harnessing the power of DORA DevOps metrics is a game-changer. By leveraging DORA key metrics, your software teams can achieve remarkable results. These metrics are an effective way to enhance customer satisfaction, mitigate financial risks, meet service-level agreements, and deliver high-quality software.
Implementing DORA Metrics to Improve Dev Performance & Productivity?
“This article is an amazing eye-opener for many engineering leaders on how to use DORA metrics. Correlating metrics gives the real value in terms of SDLC insights and that's what is the need of the hour."
“That is the ultimate goal - connecting DevOps to DORA. Super helpful article for teams looking at implementing DORA.”
Numerous metrics are available for monitoring software development progress and generating reports that indicate the performance of your engineering team can be a time-consuming task, taking several hours or even days.Through our own research and collaboration with industry experts like DORA, we suggest concentrating on cycle time, also referred to as a lead time for changes, which we consider the most crucial metric to monitor.This measurement indicates the performance and efficiency of your teams and developers. In this piece, we will cover what cycle time entails, its significance, methods for calculating it, and actions to enhance it.
Cycle Time in software development denotes the duration between an engineer’s first commit and code deployment, which some teams also refer to as lead time. This measurement indicates the time taken to finalize a specific development task. Cycle time serves as a valuable metric for deducing a development team’s process speed, productivity, and capability of delivering functional software within a defined time frame.
Leaders who measure cycle time gain insight into the speed of each team, the time taken to finish specific projects, and the overall performance of teams relative to each other and the organization. Moreover, optimizing cycle time enhances team culture and stimulates innovation and creativity in engineering teams.
However, cycle time is a lagging indicator, implying that it confirms ongoing patterns rather than measures productivity. As such, it can be utilized as a signal of underlying problems within a team.
Since cycle time reflects the speed of team performance, most teams aim to maintain low cycle times that enhance their efficiency. According to the Accelerate State of DevOps Report research, the top 25% of successful engineering teams achieve a cycle time of 1.8 days, while the industry-wide median cycle time is 3.4 days. On the other hand, the bottom 25% of teams have a cycle time of 6.2 days.
Measuring cycle time using Jira or other project management tools is a manual and time-consuming process, which requires reliable data hygiene to deliver accurate results. Unfortunately, most engineering leaders have insufficient visibility and understanding of their teams' cycle time.Typo provides instantaneous cycle time measurement for both your organization and each development team using your Git provider.Our methodology divides cycle time into four phases:
The subsequent phase involves analyzing the various aspects of your cycle time, including the organizational, team, iteration, and even branch levels. For instance, if an iteration has an average review time of 47 hours, you will need to identify the branches that are taking longer than usual and work with your team to address the reasons for the delay.
Although managers and leaders are aware of the significance of cycle time, they aren't necessarily armed with the information necessary to understand why the cycle time of their team may be higher or lower than ideal. Leaders may make decisions that have a beneficial impact on developer satisfaction, productivity, and team performance by understanding the processes that make up cycle time and exploring its constituent parts.Cycle time could increase as engineers wait for builds to finish and tests to pass before the PR is ready for review. When engineers must make modifications following each review and wait for a drawn-out and delayed CI/CD that extends the time to merge, the process becomes even more wasteful. This not only lengthens the cycle time but also causes contributors to feel frustrated.
The time it takes to open a PR increases because large-sized PRs take longer to code and, as a result, stay closed for too long. For instance, the majority of teams aim for PR sizes to be under 300 changes, and as this limit rises, the time to open the PR lengthens. Even when huge PRs are opened, they are often not moved to the code review stage because most reviewers are reluctant to do so for the following two reasons:
A high PR indicates that the reviewer put a lot of effort into the review. To accommodate a significant code review, the reviewer must plan and significantly restructure their current schedule. It takes heavy and intense effort.
Huge PRs are notorious for their capacity to add a number of new bugs.
Code comments and other forms of documentation in the code are best practices that are regrettably frequently ignored. Reviewers and future collaborators can evaluate and work on code more quickly and effectively with the use of documentation, cutting down on pickup time and rework time. Coding standards assist authors in starting off with pull requests that are in better shape. They also assist reviewers in avoiding repeated back and forth on fundamental procedures and standards. When working on code that belongs to other teams, this documentation is very useful for cross-team or cross-functional collaboration. Various teams adhere to various coding patterns, and consistency is maintained by documentation.
Teams can greatly benefit from a readme that is relevant to a codebase and contains information about coding patterns, and supporting materials, such as how and where to add logs, coding standards, emit metrics, approval requirements, etc.
Cycle time could increase as engineers wait for builds to finish and tests to pass before the PR is ready for code review. When engineers must make modifications following each review and wait for a drawn-out and delayed CI/CD that extends the time to merge, the process becomes even more wasteful. This not only lengthens the cycle time but also causes contributors to feel frustrated. Moreover, when the developers don't adhere to coding standards before entering the CI/CD pipeline can increase cycle time and reduce code quality.
Engineers may struggle with numerous WIP PRs due to an unmanaged and heavy workload, in turn reporting lengthier coding and rework times. Reviewers are more likely to become overburdened by the sheer volume of review requests at the end of a sprint than by a steady stream of PRs. This limits reviewers’ own coding time as their coding skills start deteriorating and causes a large number of PRs to be merged without review, endangering the quality of the code.
The team experiences a high cycle time as reviewers struggle to finish their own code, the reviews, and the rework, and they suffer burnout.
When teams fail to perform simple sanity checks and debugging needs before creating PRs (such as linting, test code coverage, and initial debugging), it results in avoidable nitpicks during a code review (where the reviewer may be required to spend time pointing out formatting errors or test coverage thresholds that the author should have covered by default).
So, now that you're confidently tracking cycle time and all four phases, what can you do to make your engineering organization's cycle time more consistent and efficient? How can you reap the benefits of good developer experience, efficiency, predictability, and keeping your business promises?
Start measuring the cycle time and breakdown in four phases in real-time. Start comparing the benchmarks with the industry standards.
Once you’ve benchmarked your cycle time and all four phases, you’ll know which areas are causing bottlenecks and require attention. Then everyone in your organisation will be on the same page about how to effectively reduce cycle time.
We recommend that you focus on one or two bottlenecks at a time—for example, PR size and review time—and design your improvement strategy around them.
Bring past performance data to your next retro to help align the team. Using engineering benchmarks, provide context into performance. Then, over the next 2-3 iterations, set goals to improve one tier.
We also recommend that you develop a cadence for tracking progress. You could, for example, repurpose an existing ceremony or make time specifically for goals.
Build an alert system to reduce the cycle time by utilizing Slack to assist developers in navigating a growing PR queue.
These pieces of data enable the developer to make more informed decisions. They respond to questions such as: Do I have enough time for this review during my next small break, or should I queue it?
Many organizations are adopting agile methodologies. As they help in prioritizing continuous feedback, iterative development, and team collaboration. By adopting these practices, the team can leverage their coding skills and divide large programming tasks into small, manageable chunks. Hence, completing them in a shorter cycle to enable faster delivery times.
The most successful teams are those that have mastered the entire coding-to-deployment process and can consistently provide new value to customers.Measuring your development workflow with typo’s Engineering Benchmarks and automating improvement with Team Goals and our Slack alerts will enable your team to build and ship features more quickly while increasing developer experience and quality.
Consider a world where metrics and dashboards do not exist, where your work is free from constraints and you have the freedom to explore your imagination, creativity, and innovative ideas without being tethered to anything.
It may sound like a utopian vision that anyone would crave, right? But, it is not a sentiment shared by business owners and managers. They operate in a world where OKRs, KPIs, and accountability define performance. In this environment, dreaming and fairy tales have no place.
Given that distributed teams are becoming more prevalent and the demand for rapid development is skyrocketing, managers seek ways to maintain control. Managers have started favoring “DORA metrics” to achieve this goal in development teams. By tracking and trying to enhance these metrics, managers feel as though they have some degree of authority over their engineering team’s performance and culture.
But, here’s a message for all the managers out there on behalf of developers - DORA DevOps metrics alone are insufficient and won’t provide you with the help you require.
Before we understand, why DORA is insufficient today, let’s understand what are they!
The widely used reference book for engineering leaders called Accelerate introduced the DevOps Research and Assessment (DORA) group's four metrics, known as the DORA 4 metrics.
These metrics were developed to assist engineering teams in determining two things: A) The characteristics of a top-performing team, and B) How their performance compares to the rest of the industry.
The four key DORA metrics are as follows:
Deployment Frequency measures the frequency of code deployment to production or releases to end-users in a given time frame. It may include the code review consideration as it assesses code changes before they are integrated into a production environment.
It is a powerful driver of agility and efficiency that makes it an essential component of software development. High deployment frequency results in rapid releases without compromising the software's robustness, hence, enhancing customer satisfaction.
This metric measures the time between a commit being made and that commit making it to production. It helps to understand the effectiveness of the development process once coding has been initiated.
A shorter lead time signifies the DevOps teams are efficient in deploying code while a longer lead time means the testing process is obstructing the CI/CD pipeline. Hence, differentiating elite performers from low performers.
This metric is also known as the mean time to restore. It measures the time required to solve the incident i.e. service incident or defect impacting end-users. To lower it, the team must improve their observation skills so that failures can be detected and resolved quickly.
Minimizing MTTR enhances user satisfaction and mitigates the negative impacts of downtime on business operations.
Change failure rate measures the proportion of deployment to production that results in degraded services. It should be kept as low as possible as it will signify successful debugging practices and thorough testing and problem-solving.
Lowering CFR is a crucial goal for any organization that wants to maintain a dependable and efficient deployment pipeline. A high change failure rate can have serious consequences, such as delays, rework, customer dissatisfaction, revenue loss, or even security breaches.
In their words:
“Deployment Frequency and Lead Time for Changes measure velocity, while Change Failure Rate and Time to Restore Service measure stability. And by measuring these values, and continuously iterating to improve on them, a team can achieve significantly better business outcomes.”
Below are the performance metrics categorized in
for 4 metrics –
DORA metrics are a useful tool for tracking and comparing DevOps team performance. Unfortunately, it doesn't take into account all the factors for a successful software development process. For example, assessing coding skills across teams can be challenging due to varying levels of expertise. These metrics also overlook the actual efforts behind the scenes, such as debugging, feature development, and more.
While DORA metrics tell us which metric is low or high, it doesn't reveal the reason behind it. Suppose, there is an increase in lead time for changes, it could be due to various reasons. For example, DORA metrics might not reflect the effectiveness of feedback provided during code review. Hence, overlooking the true impact and value of the code review process.
The software development landscape is changing rapidly. Hence, the DORA metrics may not be able to quickly adapt to emerging programming practices, coding standards, and other software trends. For instance, Code review has evolved to include not only traditional peer reviews but also practices like automated code analysis. DORA metrics may not be able to capture the new approaches fully. Hence, it may not be able to assess the effectiveness of these reviews properly.
DORA metrics are a great tool for analyzing DevOps performance. But, It doesn't mean they are relevant to every developer's team. These key metrics work best when the deployment is done frequently, can quickly iterate on changes, and improve accordingly. For example, if your team adheres to certain coding standards or ship software monthly, it will result in low deployment frequency almost every time and helps to deliver high-quality software.
Relying solely on DORA metrics to evaluate software teams' performance has limited value. Leaders must now move beyond these metrics, identify patterns, and obtain a comprehensive understanding of all factors that impact the software development life cycle (SDLC).
For example, if a team's cycle time varies and exceeds three days, while all other metrics remain constant, managers must investigate deployment issues, the time it takes for pull requests to be approved, the review process, or a decrease in a developer's productivity.
If a developer is not coding as many days, what is the reason behind this? Is it due to technical debt, frequent switching between tasks, or some other factor that hasn't yet been identified? Therefore, leaders need to look beyond the DORA metrics and understand the underlying reasons behind any deviations or trends in performance.
For DORA to produce reliable results, software development teams must have a clear understanding of the metrics they are using and why they are using them. DORA can provide similar results for teams with similar deployment patterns. But, it is also essential to use the data to advance the team’s performance rather than simply relying on the numbers. Combining DORA with other engineering analytics is a great way to gain a complete picture of the development process. It may include identifying bottlenecks and improvement areas. It may include including identifying bottlenecks and improvement areas.
However, poor interpretation of DORA data can occur due to the lack of uniformity in defining failure, which is a challenge for metrics like CFR and MTTR. Using custom information to interpret the results is often ineffective. Additionally, DORA metrics only focus on velocity and stability. It does not consider other factors such as the quality of work, productivity of developers, and the impact on the end-user. So, it is important to use other indexes for a proactive response, qualitative analysis of workflows, and SDLC predictability. It will help to gain a 360-degree profiling of the team’s workflow.
To achieve business goals, it is essential to correlate DORA data with other critical indicators like review time, code churn, maker time, PR size, and more. Using DORA in combination with more context, customization, and traceability can offer valuable insights and a true picture of the team’s performance and identify the steps needed to resolve bottlenecks and hidden fault lines at all levels. Ultimately, DORA should be used as a tool for continuous improvement, product management, and enhancing value delivery.
DORA metrics can also provide insights into coding skills by revealing patterns related to code quality, review effectiveness, and debugging cycles. This can help to identify the blind spots where additional training is required.
Typo is a powerful engineering analytics tool for tracking and analyzing DORA metrics. It provides an efficient solution for software development teams seeking precision in their DevOps performance measurement and delivers high-quality software to end users.
While DORA serves its purpose well, it is only the beginning of improving engineering excellence. To effectively measure DORA metrics, it is essential to focus on key DORA metrics and the business value they provide. Looking at numbers alone is not enough. Engineering managers should also focus on the practices and people behind the numbers and the barriers they face to achieve their best and ensure customer satisfaction. It is a known fact that engineering excellence is related to a team’s productivity and well-being. So, an effective way is to consider all factors that impact a team’s performance and take appropriate steps to address them.
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