Code coverage testing is a vital part of ensuring your application is properly tested, but it’s not just about hitting high numbers. It evaluates which parts of your codebase are executed by tests, helping identify untested areas that could hide critical bugs. To make code coverage testing truly effective, teams should focus on meaningful test cases, including edge cases and integration scenarios, rather than just aiming for 100% coverage. Advanced approaches now incorporate AI-driven tools, which can intelligently generate tests and analyze API behavior, helping teams prioritize critical paths. Platforms like Keploy demonstrate how automated testing can enhancecode coverage testing by providing actionable insights, making your tests both thorough and practical.