Why Is Cycle Time the Most Important Metric in Software Development?

Software development teams are pressured to deliver value quickly and consistently without compromising quality. Collecting data from the development process reveals opportunities to improve performance, but it can be difficult to decide which metrics to track.

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This article argues that cycle time is the most critical value for evaluating software delivery success. Cycle time is the time between code being committed and that change entering production. It's a good indicator of your workflow's overall maturity: high-performing teams should be able to develop and release changes quickly, resulting in a low cycle-time duration.

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Let's delve into what cycle time means and how you can optimize it.

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Understanding Cycle Time

Cycle time is the average time for a work item to progress through your software-delivery process, from first commit through to deployment. [Research from DORA](https://cloud.google.com/blog/products/devops-sre/using-the-four-keys-to-measure-your-devops-performance) has shown that the most successful teams work in short cycles, which enable code to be rapidly delivered to production.

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A lower cycle time means new features and bug fixes reach your users more quickly. This implies your workflow is responsive to new customer demands and marketplace requirements, enabling higher throughput delivery. Tighter cycles also improve the developer experience by shortening feedback loops and allowing engineers to work on smaller changes that are easier to understand.

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Cycle time has a direct influence on your DORA metrics, four values that are proven to be good indicators of software engineering performance:

• Deployment frequency increases as you can complete cycles more quickly.
• Lead time is lower because the development phase takes less time.
• Change failure rate should be reduced because you're producing a higher number of smaller changes that are easier to test.
• Mean time to recovery decreases because you're able to apply rollbacks and bug fixes more quickly during an incident.

These effects mean cycle time is the best single measure to track if you want to optimize your development team's performance. It improves your throughput and helps achieve the stability that enables consistent quality standards to be maintained.

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Why Cycle Time Matters the Most

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Cycle time is more than just a number—it's also a window into how your development process is structured. To achieve a short cycle time, your tools, working methods, and culture must effectively combine into a process that lets you ship changes with minimal friction. Collecting cycle time data is one of the best ways to understand how well your delivery pipeline is running.

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Attaining a short cycle time has positive effects on both software quality and developer experience. Condensed cycles support focus, feedback, and efficient iteration by ensuring changes are compact enough to be easily understood.

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Focus and Prioritization

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Sticking to short cycles encourages tasks to be assigned as small, well-defined work items. Breaking large projects into smaller units makes work more approachable and easier to prioritize, allowing developers to focus intently on specific parts of a solution. Discrete tasks can be developed and reviewed more quickly, giving engineers a clear sense of progress that helps sustain morale.

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Early Feedback and Quality Improvement

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Small tasks tackled in short cycles reduce the turnaround time for feedback. QA teams, customers, and business stakeholders can spot issues earlier so that they can be corrected before additional work is layered on top. This makes it easier to deliver high-quality software that continually improves as it's developed.

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By contrast, longer cycles often demand an extended review and testing phase at the end, causing unpredictable scheduling changes.

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Improved Developer Experience

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Reduced waiting times, clearer task definitions, and tighter feedback loops improve developers' everyday experience. This contributes to a more productive working environment where devs have a clearer sense of what's happening and can easily see the results of their activity. Work moves more quickly through the pipeline and there's less complexity in each change, helping accelerate review and testing phases.

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Enhanced Customer Satisfaction

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Lowering the cycle time means new features and fixes can reach customers sooner. This results in happier users who see your product advancing quickly to meet their needs continually. Quicker rollouts make it more likely that customers will stay with your solution instead of trying a competitor that delivers value less frequently in unpredictable releases.

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Ease of Iteration

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Keeping cycles short promotes iterative feature delivery. You can quickly launch an MVP and then build additional capabilities in future cycles. This allows you to reach the market faster, begin collecting feedback earlier, and prioritize further development based on your users' actual requirements.

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Developers also benefit from being able to work on smaller tasks that don't always demand perfect code on day one.

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Analyzing Cycle-Time Data

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Regularly analyzing cycle-time data allows you to spot development bottlenecks and potential improvement opportunities.

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To begin inspecting the trends in your cycles, you need to gather data from the development platforms you use. You can automate this process by choosing tools that integrate directly with your code hosts, work-management systems, and deployment platforms to extract the time it takes for changes to reach production.

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Cycle-time values on their own aren't always immediately useful. You need to look deeper into the data to extract actionable insights because delays can be distributed differently between the stages in each cycle. For example, you might find that development tasks are being completed efficiently, but changes then take too long to be reviewed or tested. Therefore, it's important to use solutions that allow you to visualize and filter cycles based on stage.

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Similarly, it's helpful to analyze cycle-time data separately for different types of work. Major feature developments will generally tolerate longer cycle times than bug fixes and security patches, for example. Only looking at your overall average cycle length is an impractical way to assess how well you respond to urgent development demands.

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The report below from DevStats illustrates an optimal way of displaying cycle-time data. You can view the report by day, week, or month and see how much time has been spent coding, picking up tasks, reviewing PRs, and managing the deployment process. It's also possible to filter by team, participants, and repository branch so that you can drill down to the cycles associated with specific project activity:

This report makes it easy to visualize where development is becoming bottlenecked and cycle lengths are increasing. For example, if you see that deploy time is increasing while other tasks remain relatively static, the insight could prompt an evaluation of your CI/CD pipeline’s efficacy. Alternatively, an elevated pickup time could indicate that the team has reached its capacity and is unable to accept further assignments.

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DevStats automates the collection of cycle-time data by connecting directly to your GitHub, GitLab, and Bitbucket repositories. It connects your work to cycles based on the pull requests you open, comment, and merge.

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Ways to Optimize Cycle Time

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Now that you understand the importance of cycle time and how to analyze it, let's consider techniques for shortening your cycles sustainably.

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Use Code Reviews to Identify Issues Early

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Code review is a vital tool for keeping cycles short. An effective review process allows you to find and resolve potential issues earlier in the development process by getting more eyes on your code.

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Code review is often deferred until development is complete. However, soliciting feedback more regularly results in smaller reviews that are easier to deal with, cutting down on cycle time. Reviewers will appreciate having fewer changes to inspect each cycle—research suggests the ideal code review length is under 400 lines and ideally closer to 250 lines—while developers are less likely to be tasked with making major changes that would undo previous work. This enables cycles to be completed more quickly as noncritical issues found during the review can be dealt with as tasks in the next cycle.

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The review process also needs to be optimized so reviews can be completed efficiently. Tools that alert developers when reviews are requested or feedback is ready help ensure completed code isn't forgotten. This makes it less likely that cycles will overrun due to changes getting stuck awaiting appraisal.

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Automate Complex Processes with CI/CD

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Continuous integration and delivery (CI/CD) pipelines let you automate complex development processes to reduce cycle time.

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If developers have to manually apply actions to run tests, conduct security scans, and launch deployments, they're likely wasting time while waiting for results. CI/CD supports developers by running these repetitive tasks on a central server. Jobs are created with dependably consistent configuration, avoiding the environmental discrepancies that occur when devs use locally hosted tools. The correct jobs are also started automatically after code is pushed, leaving engineers free to focus more fully on development.

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Setting up a CI/CD implementation should be seen as an investment—it requires time up front, but then it lifts work out of your cycles throughout your project's life span. This makes day-to-day development much more efficient, enabling a sustained reduction in cycle time.

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Break Down Tasks into Smaller Work Items

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Splitting features into multiple smaller tasks facilitates simpler iterative development. You can complete individual work items more quickly and then build upon them in future cycles. This reduces cycle time and enables feedback to be collected earlier.

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Understanding when and how to split tasks can be difficult, but you can start by assessing the minimum functionality required for each new development sprint. Building those pieces, delivering them to users, and then continuing the development in another cycle keeps the cognitive burden on developers low and provides visible results more quickly. You'll also be more likely to ship what customers need because they'll be able to start shaping its development sooner.

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Address Development Bottlenecks by Analyzing Cycle-Time Data

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Recommendations—such as smaller tasks, CI/CD, and streamlined code reviews—help you on your journey to shorter cycles, but other bottlenecks will be unique to your team and how you work. It's crucial to regularly analyze cycle-time reports and identify any changing trends that could expose issues in how you work. A gradual increase in cycle duration might mean developers are losing motivation, no longer supported with the right tools, or struggling under the pressure of their workload, for example.

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Talking to developers can reveal previously unknown problems that allow you to optimize your processes better. It's also important to assess how well teams are collaborating; weak communication methods or delays waiting for stakeholders to respond will extend your cycles. Removing these roadblocks helps your workflow run smoothly with a predictable low cycle time.

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Conclusion: Use Cycle Time to Drive Software-Delivery Process Improvements

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Cycle time is the most important metric for gauging the performance of software development teams. A low cycle time means changes can be rapidly delivered to production, improving throughput and keeping both developers and customers happy. The ability to ship more quickly also promotes iterative working methods based around smaller and safer changes, aiding fast recovery if you need to roll back a release.

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Monitoring cycle time allows you to detect potential workflow inefficiencies and assess how developer performance is evolving. A data-driven approach to development promotes continuous improvement by providing visibility into what's happening in your processes. You can then take steps to remove obstacles that are extending your cycle time—such as increasing your use of automation or breaking issues down into smaller tasks—and repeat your measurements to check if the change has been effective.