Developers often focus on achieving a high code coverage percentage, believing that it automatically translates to better test quality. But is that really the case? While coverage metrics (like line, branch, or function coverage) indicate how much of your code is executed during testing, they don’t always reflect how well that code is tested.

For example, you might have 90% code coverage but still miss critical edge cases, exception handling, or integration flaws. In reality, code coverage should serve as a diagnostic tool—helping teams identify untested areas and guiding them to improve meaningful test depth rather than chase arbitrary numbers.

Modern tools integrated into CI/CD systems provide deeper insights, correlating coverage data with defect density, code complexity, and test redundancy. Platforms like Keploy go a step further by automatically generating test cases from actual API calls, bridging the gap between code coverage metrics and real-world usage.