LLM-Powered Test Generation: Using AI to Find Bugs in Your Software, Not Just Write It
Most engineering teams using LLMs are focused on code generation — getting the model to write features faster. But there's a higher-leverage application that gets far less attention: using LLMs to generate the tests that find bugs humans miss. Not testing the AI — testing your software with AI.
The pitch is compelling. Hand-written test suites are shaped by human imagination, which means they cluster around the scenarios developers think of. LLMs explore state spaces differently. They generate inputs and edge cases that feel alien to the original author — and that's precisely where undiscovered bugs live.
But the reality is messier than the pitch. Raw LLM-generated tests fail compilation more than half the time. Over 85% of failures come from incorrect assertions. And integrating non-deterministic generation into a deterministic CI pipeline creates its own class of engineering problems. Here's how to make it work anyway.
