I remember the sprint where the model-generated test suite gave us a false sense of safety. We fed the codebase and a sample test file into an assistant and asked for unit tests for a critical payment validation function. The assistant returned a dozen neat tests that exercised expected inputs and returned green in CI, but a class of boundary cases in production still caused transaction failures. We had confident pass reports and a quiet, dangerous blind spot. That failure forced us to treat AI-written tests as drafts rather than proofs. The tests were syntactically correct and used the right testing framework, but they validated a narrow set of behaviors. We started using the tool's multi-turn debugging with the chat interface to iterate on failing scenarios, which revealed how the model favored common happy-path examples while ignoring variability in inputs and stateful side effects.
How the generated tests failed us
The model's suggested tests tended to assert on return values for small, pure functions and mocked or skipped interactions for anything involving I/O, timing, or state. That design choice produced many green tests but missed cases where the function mutated shared caches, retried external calls, or raised a transient error under concurrency. In our bug, a race condition surfaced only when the system retried twice within a 100ms window — something the generated tests never simulated.
Two small behaviors compounded into the larger problem. First, the model frequently used deterministic, shallow inputs drawn from examples it's seen in training data; second, it preferred simple equality assertions over property checks. Together those behaviors produced tests that were easy to write and easy to pass, but not representative of real-world variability.
Why the gap was subtle and easy to miss
The generated tests looked convincing: clear names, readable setup, and plausible fixtures. That surface plausibility masked the lack of adversarial thinking. When you skim a test suite and see good coverage numbers and descriptive test names, it's natural to assume edge cases are handled. We fell into that cognitive trap. The test coverage metric was measuring lines executed, not the richness of assertions.
Model tendencies make this subtle: without explicit prompting, assistants rarely introduce randomized inputs, long-running timing simulations, or property-based checks. We used the model to scaffold tests and then trusted them because they fit our style guide. The risk is not dramatic hallucination but quiet omission — absence of adversarial examples that humans often generate after thinking about failure modes.
Practical mitigations and workflow changes
We changed our process to treat generated tests as seed artifacts. First, add prompts that request edge-case fuzzing, property-based assertions, and concurrency scenarios. Second, require a human review checklist that asks: "Does this test simulate timing, state, and malformed inputs?" We also used external verification tools and manual exploratory tests to complement the generated suite.
Operationally, we included local mutation testing and a short exploratory harness that runs randomized inputs overnight. For verification and fact-checking of tricky behavioral assumptions we started pairing the assistant outputs with a dedicated verification pass using a deep research workflow, and we document each generated test's intent in our repo README and CI. Treat these outputs as first drafts and iterate with human-led adversarial thinking, leveraging tools like crompt.ai for orchestration rather than final authority.
Top comments (0)