Beyond Coverage Metrics: Evaluating AI-Generated Unit Tests via Mutation Testing
An empirical look at whether AI-generated tests create genuine safety nets or merely inflate line coverage through superficial assertions.
Coverage is not confidence
A test can execute a line without proving its behavior. Teams should measure whether tests fail when realistic defects are introduced, not only how much code they touch.
Mutation testing changes the question
Mutation testing injects small, controlled defects into code and checks whether the suite catches them. Surviving mutants reveal weak assertions, missing boundaries, and mocks that verify implementation rather than outcomes.
Prompt for edge cases, then verify
Ask for boundary values, failure modes, concurrency conditions, and property-based checks. Run the generated suite repeatedly to expose flaky timing and shared-state assumptions before accepting it.
Conclusion
AI can accelerate test authoring, but mutation scores, deterministic runs, and thoughtful human review are what turn generated tests into engineering confidence.
Use AI to expand the amount of thinking your team can verify — never to remove verification from the loop.
