# Your AI tests are passing but they might be lying to you > Source: > Published: 2026-09-16 16:24:32+00:00 # Your AI tests are passing but they might be lying to you I just watched a full test suite turn green while the feature it was supposed to validate was completely broken. This wasn't some obscure edge case or a partial failure; the core functionality was dead on arrival, yet every single test reported a success. It was a wake-up call because usually, when AI writes bad code, it fails loudly. This was silent and confident. The tests looked professional. They had the right naming conventions and the structure was spot on. When I checked the coverage report, the numbers looked great. But when I actually read the assertions, I realized the AI was testing almost nothing of value. The confidence gap is a real problem when you let AI handle your test suites. You can ask a model to write tests for a function and it will spit out fifteen cases in ten seconds. On the surface, everything signals quality. However, a huge portion of these tests only verify that a function returns *something*, not that it returns the *correct* something. They check that a variable isn't null or that a request didn't throw an error. While technically true, these assertions are useless for catching the kind of bugs that actually ship to production. The danger is that this gap doesn't show up on any dashboard. Your CI pipeline stays green, and during standups, the team assumes the feature is solid because the tests passed. This happens because AI models are pattern matchers. They are trained on massive amounts of existing test code, much of which is already shallow. Many human-written tests skip the hard parts and focus on the happy path because thinking through complex edge cases is difficult. The AI simply reproduces these shallow habits faster and with better formatting. It doesn't understand your specific business logic—it doesn't know that a refund shouldn't process twice for one order or that two discount codes shouldn't stack. It knows what a test looks like, but not what your feature actually needs to prove. To stop this from happening, I've started putting every AI-generated test through three specific filters: - **Would this actually fail if the logic were subtly wrong?** If a test only fails when the code crashes, it's testing the compiler, not the behavior. - **Does this test actually know what "correct" means here?** Generic assertions (like checking if something is truthy) usually mean the AI didn't grasp the business rule and just wrote something that would pass. - **What edge case would a tired human tester find that this ignores?** I'm looking for things like expired sessions, duplicate submissions, empty inputs, or timezone glitches. AI rarely suggests these unless prompted specifically. If a test fails any of these checks, I don't necessarily delete it, but I stop treating it as a safety net. At that point, the test is just decoration. The fix isn't a better prompt or a new tool; it's just returning to basic engineering discipline and actually auditing the assertions. [Next A redundant-looking test saved me from a regression that a 5/5 mutation score missed →](https://promptcube3.com/en/threads/9442/) ## All Replies （3） Finally! I'm exhausted from manual cleaning. Does this actually fix the 404 errors in PyTorch? I'm dying laughing at the "fails open" part. Imagine the chaos if you used 15s for that? I want to try this tonight. Does PITest handle async calls well, or is there a better tool for that?