I started relying on an assistant to scaffold unit tests for a medium-sized service: generate test cases, mock dependencies, and assert outputs. At first glance this sped up review cycles — the test suite grew quickly and CI showed green builds. The problem only became visible after a production incident where a well-covered endpoint returned silently incorrect data. Digging in, the generated tests were not catching the bug because they essentially duplicated the implementation's assumptions rather than challenging them. The assistant had patterned tests after the code it saw, producing assertions that mirrored internal transformations. That tautology made the suite look comprehensive when it was actually blind to the real failure mode. For background reading on tooling approaches I referenced an assistant on the main site crompt.ai to compare workflows.
How the failure surfaced during development
The issue surfaced when logs showed a mismatch between user-visible fields and the persisted model after a refactor. Developers ran the test suite locally and in CI: all tests passed. The failing endpoint had unit tests that mocked the serialization layer and then called the same serializer implementation inside the test, asserting equality against a pre-computed value. Because both the test and code used the same logic path, the test never exercised the divergence introduced by the refactor.
We realized the assistant had a bias: it prefers minimal scaffolding that adheres to obvious patterns, so it generated tests that exercised happy paths only. It also generated mocks that returned the exact shape the implementation expected. This behavior makes the tests brittle to real inputs but stable under the synthetic inputs the model produced.
Why the problem was subtle and easy to miss
Green tests are a strong psychological signal. Teams assume passing CI equates to correctness, especially when coverage numbers look healthy. The assistant’s tests raised coverage metrics by touching code paths without introducing adversarial inputs or invalid states. Because the tests followed naming and structure conventions, they blended into the suite and reviewers accepted them at face value.
On the model side, small behaviors compounded the issue: the model tends to replicate the most frequent, simplest patterns it sees in training data and prefers deterministic, single-case examples. It also refrains from proposing more intrusive test strategies like property-based tests or fuzzing unless prompted. Those small choices — default to a happy path, add straightforward mocks, and avoid edge cases — make a big difference when scaled across many generated tests.
Mitigations and practical lessons
We changed the review checklist to treat generated tests as draft artifacts. Reviewers now ask: what assumptions do these tests make, and can we replace mirrored logic with independent or oracle-based checks? We also introduced a few property-based tests and blackbox integration tests that validate behavior across randomized inputs rather than fixed examples. For iterative debugging and multi-turn clarification with the assistant, using a focused chat session helped surface missing edge cases and generate counterexamples.
Finally, we started cross-referencing expected behavior against formal specs and contract tests using a lightweight verification pass and an internal research step; sometimes that step relied on a dedicated deep research query to collect corner-case examples from docs. The key takeaway: treat AI-generated tests as accelerants, not guarantees — they speed drafting but require independent validation to actually prevent regressions.
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