Metrics for continuous integration

Frequency of integration: The average number of times developers integrate their code with the main codebase. This metric provides insight into the team's adherence to Continuous Integration practices, facilitating early issue detection and improved collaboration. Improve this metric by educating developers on the benefits of regular integration and implementing automated reminders or tools that encourage developers to integrate changes after a set number of code modifications or elapsed time. Track this metric using logs from the version control system to observe how often developers are merging code to a main releasable branch.

Build success rate: The ratio of successful builds to total builds, expressed as a percentage. This metric helps teams understand the stability of their build infrastructure, quality of code changes, and the effectiveness of their tests. Track the number of successful builds over a period of time and divide by the total number of builds, then multiply by 100 to get the percentage.

Pipeline stability: The percentage of build failures due to reasons other than code errors. This metric measures the reliability of the continuous integration pipeline, including its configuration and infrastructure. A high rate of such failures indicates that your continuous integration pipeline may need attention. To improve pipeline stability, routinely audit and update the pipeline, ensure consistent build environments, and reduce external dependencies that are prone to failure. Track this metric using continuous integration pipeline logs to calculate the percentage of build failures that are due to infrastructure or configuration issues compared to the total number of build failures.

Mean time to build (MTTB): The average time required to run a complete successful build cycle, from when a code change triggers the build to its full validation. Using this metric, teams can pinpoint bottlenecks in their build process. Improve this metric by minimizing dependencies, leveraging caching, running tasks in parallel, and using more powerful or distributed build systems. Measure the duration from when the build is triggered to its completion, considering only successful builds, and calculate the average over a defined period.