How to Use SDLC Gates to Catch Bugs in AI-Generated Test Code

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AI tools can generate test code rapidly, but without proper checks, these tests may include hallucinated assertions, irrelevant logic, and silent regressions that only appear in production. A proposed framework treats AI-generated tests as untrusted input and routes them through four mandatory quality gates before merging. The four gates cover execution validity, meaningful code coverage, behavioral accuracy via mutation testing, and drift detection using snapshot or approval testing. Each gate is enforced through CI pipelines using tools like GitHub Actions, Jest, Stryker, and coverage utilities. The approach aims to let teams benefit from AI-assisted testing while preventing low-quality or misleading tests from reaching production.

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ProgrammingDEV Community ·

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