Mutation Testing

Series Orientation: This article is Part 3 of the AI Code Review & Vibe Coding series, examining the unique failure modes of AI-generated code. For the broader business context, see the Series Executive Summary. When engineers first review AI-generated code, they often encounter a counterintuitive phenomenon: the code looks right. It passes compilation. The tests are green. The function signatures are clean. The variable names are descriptive. And somewhere inside, there is a logic error that will silently corrupt your data, or a missing authorization check that will expose every user record to the first person who thinks to try a simple query manipulation. ...

Series Orientation: This article is Part 4 of the AI Code Review & Vibe Coding series, focusing on building an automated multi-agent quality gate pipeline. For the bug taxonomy that informs these gates, see Part 3 — AI Code Bug Taxonomy. The software industry has spent two years discovering that the productivity problem of AI coding is not generation speed — it is verification speed. AI coding tools are extraordinarily effective at generating code quickly. GitHub Copilot internal data shows task completion up to 55% faster for scoped coding tasks. The bottleneck that this creates is not in the generation phase. It is in the review phase, where PR volume has increased by 20–90% across high-adoption teams while review capacity has not scaled at the same rate. ...