AI code needs different testing than human code. The mistakes are different patterns. I developed a testing workflow over 8 projects that catches AI-specific bugs without wasting time on irrelevant tests.
What types of tests catch AI-specific bugs?
Input validation tests catch the most AI bugs — the AI often assumes clean inputs. Edge case tests catch the second most — the AI writes for the happy path. Security tests catch the third most — the AI uses insecure patterns like raw SQL or eval(). Unit tests are least useful because the AI writes syntactically correct code that fails on edge cases, not on logic.
How do you automate testing for AI-generated code?
I use a pre-commit hook that runs three checks: ESLint security plugin, TypeScript strict mode, and a custom prompt that asks the AI to find 3 potential issues in its own code. The AI finds about 2 real issues per run. Over 100 generations, that is 200 issues caught before commit. This is part of my code validation workflow.
Leave a Reply