Mutation Testing Catches What Code Coverage Misses in AI Systems

AI-generated code can pass high code coverage metrics while containing subtle logic errors because AI lacks domain-specific knowledge of system requirements. The article presents a real production case where AI-generated payment reconciliation code achieved 92% test coverage yet used object reference equality instead of business key equality, causing silent data duplication bugs that only surfaced days later. Mutation testing—which deliberately introduces code mutations to verify whether tests detect them—is crucial for AI code validation because it ensures semantic correctness rather than mere structural execution.