Testing AI-Generated Code Requires a Different Default Posture
Test-driven development was always more useful as a design discipline than a testing strategy. Writing the test first forced clarity about what the code was supposed to do before any implementation decisions were made. The red-green-refactor loop was valuable because it built understanding incrementally.
AI-assisted development has resurfaced this distinction in an unexpected way. When code can be generated quickly from a specification, the temptation is to generate the implementation first and the tests afterward — or to generate both simultaneously and treat passing tests as evidence of correctness. Both approaches share a failure mode: the tests are written by something that already knows how the implementation works, which means they tend to test the implementation rather than the behavior. Tests that emerge from the same generation context as the code they test are not independent verification. They are documentation of the assumptions the generator made.
The practical implication is that tests for AI-generated code need to be written with adversarial intent — specifically designed to find the edge cases and failure modes that a fluent but contextually limited generator might miss. This is less about coverage percentage and more about the origin of the test cases. Tests derived from the requirements and domain knowledge, before or independent of implementation, are much more valuable than tests derived from the generated code itself.
Teams that have adopted a pattern of writing acceptance-level test cases from requirements before engaging AI generation have found that this practice catches a significant category of errors early: cases where the generated implementation is internally consistent but does not match the actual requirement. The test fails not because the code is buggy but because the code was built to a wrong or incomplete specification.
This is test-driven development in its original sense — as a requirements clarification tool first and a correctness verification tool second. The AI era has not made it obsolete. It has made the original argument for it more compelling.