When teams talk about improving test quality, “test code coverage” often becomes the first metric to check. It measures how much of your codebase is executed during testing—but does a high percentage always mean your software is truly well tested?

While test code coverage is an excellent starting point for identifying untested parts of the code, it doesn’t always reflect the effectiveness of your tests. For instance, you can have 95% coverage and still miss critical logic flaws, edge cases, or integration bugs. Many modern QA teams now pair coverage metrics with mutation testing or AI-driven test generation to measure how resilient their code really is.

Tools like Keploy, for example, go beyond surface-level metrics by automatically generating and validating tests based on actual API behavior—helping teams achieve not just higher coverage, but more meaningful coverage.

In your experience, how do you balance quantitative metrics like code coverage with qualitative measures of test depth and reliability?test code coverage