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Toward Automated Verification of Unreviewed AI-Generated Code — 68pts, 55cmts
Peter Lavigne proposes shipping AI-generated code with zero line-by-line human review by layering four automated verification mechanisms: property-based testing via Hypothesis (100+ semi-random inputs including edge cases), mutation testing via mutmut (deliberate operator/constant corruption to expose coverage gaps), side-effect detection, and static analysis. He argues the remaining space of undetected invalid programs becomes 'small and hard to land in by accident,' with a working reference implementation at github.com/Peter-Lavigne/fizzbuzz-without-human-review. The setup overhead still exceeds manual review today, but the framework is the most concrete automated path to trusting AI output in production.
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