The Test Looked Redundant. The Ninth Bug Needed It.
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In-depth mutation testing case study, directly applicable to SDLC and quality assurance practices.
A test that appeared redundant against a catalogue of eight wrong implementations became the only check that caught a ninth bug, demonstrating that mutation testing's generated candidates may miss critical distinctions. The author revised their recommendation for evaluating AI-generated tests: instead of just asking which plausible wrong implementation a test rejects, run the suite against a broader catalogue of mistakes and count rejections, but note the denominator still needs review. The analysis used a Python fixture with mutmut 3.7.0 and CPython 3.14.6, with code and results on GitHub.