How can you measure the success of your CI implementation?

In my experience as a System Analyst at Silver Touch Technologies Ltd, I've found that measuring the success of a CI implementation can be effectively gauged by observing build frequency. At one time at work, we increased our deployment efficiency by 25% by adopting a rigorous Git workflow. This led to more frequent builds, a key indicator of successful CI, as it reflects regular code integration, minimizing conflicts and bugs. However, it's also crucial to ensure the builds are quality and necessary, avoiding wasteful resources. This real-world application of CI principles has proven quite beneficial in maintaining a streamlined and efficient development process.

Monitoring the duration of builds is vitally important. Slow builds reduce developer velocity, team velocity and can have negative impacts on overall team morale if they become prohibitively slow. For Android/Kotlin projects, our team has made extensive use of Devlocity (formerly Gradle Enterprise) to monitor and optimize our build times. As these times have come down, we are able to spend less time context switching, less time in PRs, and validate more things on each build.

One time at work, as a Team Lead at Silver Touch Technologies Ltd, I led the refinement of CI/CD processes utilizing TeamCity and Jenkins, which directly influenced the build duration. We aimed to keep builds under 10 minutes, and by streamlining development workflows and automating key stages, we achieved a 33% boost in developer productivity. In my experience, monitoring the build duration is crucial for CI success, as it reflects the efficiency and stability of the build process. One thing I have found helpful is using parallelization, caching, or incremental builds, which significantly improved our build duration, allowing for quicker feedback and faster deployments.

One time at work, while leading the team at Silver Touch Technologies, we focused on enhancing our CI processes. We measured our CI success by monitoring the build failure rate. Initially, the rate was high, indicating frequent errors. In my experience, this prompted a rigorous code review process, emphasizing Kotlin best practices, which substantially improved code quality and reduced the build failure rate. One thing I have found helpful is setting a benchmark for keeping the build failure rate under 10%. By doing so, we ensured a high level of code quality and early detection of issues, significantly boosting our team's productivity and app performance.

One time at work, while leading the team at UNITASK TECHNOLOGIES (OPC) PRIVATE LIMITED, I focused on implementing MVVM design patterns and refining CI/CD processes with TeamCity and Jenkins. This led to a 25% increase in developer productivity. In my experience, monitoring test coverage was crucial. By aiming for and maintaining over 80% test coverage through various automated tests, we ensured the app's functionality and reduced defects. One thing I have found helpful is continuously evaluating test coverage metrics to identify gaps in testing and improve the relevance and effectiveness of our tests, thereby enhancing the overall app quality and performance.

Quality and thoroughness in testing go beyond just achieving a high percentage of test coverage. While test coverage is a valuable metric, it's equally important to focus on the effectiveness and relevance of the tests themselves. Ensuring that tests cover a diverse set of scenarios, including edge cases, error conditions, and different input values. Only then we can take test coverage as a measure of our CI success.

Across the last three companies I’ve worked at, the most valuable metric for CI was staging deployment frequency and rollback frequency. App store approval processes and usability considerations (updating three times a day isn’t a good thing for users to notice, and they will) often mean it doesn’t make sense to ship every last little feature the second it is available. Rollbacks measure bad deployments, usually caused by someone forcing a non-standard deployment. Rollbacks should be exceptionally rare because it means that the development process failed in some way, and at least for me, it usually is when someone goes off process to get something out the door and fails.

One time at work at UNITASK TECHNOLOGIES, we refined our CI/CD processes using TeamCity and Jenkins, which led to a 25% boost in developer productivity. In my experience, measuring the success of CI implementation was evident through the increased deployment frequency. We aimed for and achieved weekly deployments, significantly speeding up our delivery of value and responsiveness to feedback. One thing I have found helpful is tracking deployment frequency as a key metric. This not only demonstrated our CI effectiveness but also highlighted areas for improvement, ensuring we continuously leveraged our CI process to its fullest potential.

Mean Time to Recovery (MTTR) MTTR measures the average time taken to recover from a failed build or deployment. A lower MTTR signifies a more efficient CI implementation, indicating that teams can swiftly identify issues, troubleshoot, and restore the system to a stable state. Monitoring MTTR helps in gauging the responsiveness and effectiveness of the team's problem-solving capabilities within the CI pipeline.

Code Quality Metrics: This involves assessing the quality of the codebase by analyzing factors such as code complexity, adherence to coding standards, and the number of code smells or anti-patterns. Tools like SonarQube can be used to automate this process, providing insights into maintainability, reliability, and security of the code. By tracking these metrics, teams can ensure that the CI process not only speeds up software delivery but also maintains a high standard of code quality, ultimately leading to a more robust and efficient application.