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A green test suite doesn't always mean your code is correct.
One thing I've been thinking about while building AI-powered developer tools is how easily a test can pass without actually verifying the behavior that matters.
For example, a test might call a method and finish with assertTrue(true). Everything looks green, but the test hasn't proved that the expected behavior works.
AI coding assistants make writing code and tests faster. That makes it even more important to ask a few questions before merging:
Does the test verify the behavior the PR changes?
Are mocks hiding the behavior we're supposed to test?
Would the test catch a broken implementation?
Are we reviewing the wider impact, not just the changed lines?
These questions have influenced how I'm building CodeGuard AI, a self-hosted GitHub PR reviewer that analyzes code changes alongside repository context and supports custom team rules.
I'm curious about other developers' experiences:
What's the most misleading passing test you've encountered in a real project?
I'd love to hear how you caught it.
Exactly , that's the dangerous part. If AI introduces a bug and then writes a test based on the same mistaken assumption, both the implementation and the test can agree while the actual requirement is still broken.
That's why I think reviewing what a test truly verifies matters...
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