AI-written tests fail when models miss context or write low-quality checks
dotey · x · 2026-07-24
dotey argues that AI-written tests are only useful when the tests are actually good, because bad tests become technical debt rather than help.
He says the main failure modes are:
- the AI misunderstands the requirement or context and writes the wrong test
- the AI cannot produce high-quality tests, which was especially common with earlier models and can even include code tampering to satisfy the test
His recommendation is simple: humans must review the interpretation, and better models should be used for test generation. AI is most valuable when it can repeatedly self-correct against a solid test suite.
Related event: The Pitfalls of AI in Code Refactoring and Testing(2 posts)→
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