Our AI Testing Strategy

When 60% of your codebase is AI-generated, your testing strategy needs to account for different failure modes than hand-written code.

How AI code fails differently

Human-written bugs tend to be logic errors — the developer understood the requirement but made a mistake. AI-generated bugs are more often misunderstanding errors — the code is internally consistent but solves the wrong problem.

Our approach

We test AI-generated code at three levels: behavioral tests (does it do the right thing), boundary tests (does it handle edge cases), and integration tests (does it play well with existing code).

Behavioral tests first

For every AI-generated function, we write a test that verifies the business requirement, not the implementation. This catches the most common AI failure mode: code that works but doesn't match intent.

Edge cases are critical

AI tends to handle the happy path well and miss edge cases. Empty arrays, null values, concurrent access, Unicode input — these are where AI-generated code most often fails silently.

Integration testing

The biggest risk with AI-generated code is at the boundaries. The function works in isolation but makes assumptions about the calling context that don't hold. Integration tests catch these.

Automation

We use AI to generate test cases too, but with human review. The AI is good at generating test structure but often misses the adversarial cases that matter most.