How do you catch subtle logic errors when AI code and tests match perfectly?
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Run into an issue recently where AI wrote bad logic for a discount feature, but generated passing tests for it anyway because the model made the same wrong assumption in both places.
Now I review test files first to verify the assumptions before looking at the implementation.
Saved me a ton of review time this past week.
What workflow tweaks have helped you review model generated code faster?
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I usually try a few unusual cases too. The normal one can pass while a small logic problem stays hidden
I have started checking the test assumptions first too because matching tests can stil poss when the underlying logic is worng especially with ai generated code
the scary part is when the code and tests make the same mistake. everthing looks ine until someone checks the real result.
I've pretty much given up on reviewing every line, it's just not realistic anymore. I lean on end-to-end tests instead, since they check what actually happens in the app. And when I'm stuck I throw the problem at Codex or Grok too.