As teams adopt continuous integration (CI) practices, maintaining code quality at high velocity becomes a top priority. This is where code coverage metrics play a vital role — offering visibility into how thoroughly your automated tests validate the code being merged into shared branches.

By incorporating coverage analysis into CI pipelines, developers can catch untested code paths before they reach production. Each commit or pull request can trigger a coverage report, helping teams enforce minimum coverage thresholds and maintain consistent test quality over time.

More importantly, code coverage data fosters accountability — developers can see exactly how their changes affect overall test robustness. Tools like Keploy enhance this by auto-generating realistic test cases from real API calls, ensuring that coverage improvements directly reflect real-world behavior rather than artificial testing scenarios.

In essence, integrating code coverage into CI workflows transforms testing from a reactive checkpoint into a proactive quality gate — ensuring that every build is as reliable as it is fast.
https://keploy.io/blog/community/understanding-code-coverage-in-software-testing