The harmless mistakes were easy to spot. The dangerous ones were tests that proved the wrong thing about the riskiest code.
Most mistakes in generated tests are boring: a weak assertion, a missing edge case. You find them in review and fix them in a minute.
The dangerous ones are different. They sit on the code that can hurt you (money, time, security, concurrency, transactions), and they produce a green checkmark that makes the risk look handled. Here are the five categories I’d now check first in any AI-generated test suite, with code for each.
1. Tests That Lock In a Bug as “Expected Behavior”
What Happens
If you generate tests from existing code, the model has no way to know a behavior is a bug. It documents what the code does.
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