Why Test Coverage Metrics Lie and What to Use Instead

Lucas and Luna explain why test coverage percentages, especially line and branch coverage, give teams a false sense of security. They dive into the concept of mutation testing — where small changes are injected into code to see whether tests detect them. Using real examples from a FinTech startup that discovered critical gaps despite '100% coverage', they show how mutation score reveals the true effectiveness of your test suite. The episode also covers the practical challenges: execution time, false positives called 'equivalent mutants', and how teams integrate mutation testing into CI pipelines without slowing down deployments. By the end, listeners understand why a mutation score of 80% is more meaningful than line coverage of 95%, and how tools like PIT and Stryker make this approach accessible for Java, JavaScript, and Python teams. #MutationTesting #TestCoverage #SoftwareTesting #QA #Automation #PIT #Stryker #Java #JavaScript #Python #CI #DevOps #FinTech #Technology #FexingoBusiness #BusinessPodcast #LucasAndLuna #TestEffectiveness Keep every episode free: buymeacoffee.com/fexingo