Why AI Test Generation Shifts Work From Writing Cases to Reviewing Them

A post by 2SD Technologies argues that the real bottleneck in software testing is not execution speed but the enumeration of edge cases that requirements never mention. A single acceptance criterion — such as applying a promo code at checkout — typically yields only three obvious test cases, yet at least six high-value edge cases exist across categories like idempotency, boundary conditions, and race conditions. The author contends that language models can generate these additional cases from requirements and code, making enumeration tractable for the first time. However, generated cases only add value if they are traceable to requirements, properly prioritised, maintainable, and capable of failing loudly when uncertain. The piece concludes that AI shifts the tester's role from authoring cases to reviewing them, which the author frames as a more valuable use of human judgement — though only if teams have the capacity to review what is generated.