Originally published on tamiz.pro.
Large language models (LLMs) are transforming how development teams generate unit, integration, and contract tests. A developer types a brief description of the desired behavior, and within seconds, the LLM produces syntactically valid, semantically plausible test code. This speed feels revolutionary. But beneath the surface lies a subtle danger: AI-generated tests are not truly testing the system — they are testing the AI's own biases, assumptions, and blind spots.
The core issue is that LLMs are trained on vast corpora of existing code, documentation, and natural language. Their outputs reflect statistical patterns rather than rigorous logical reasoning. When an LLM generates a test, it is essentially extrapolating from what it has seen before — not from a formal specification of correctness. This means the generated tests often reinforce the same assumptions that the original code was built upon, creating a feedback loop where bugs hidden in plain sight remain undetected.
The Nature of AI Blind Spots in Testing
There are several layers to the blind spots that AI introduces into testing:
