LLM-written code shifts confidence from review to evidence
Software teams may increasingly judge LLM-generated code by the evidence around it, not by whether a human read every line. Mark Seemann examines Robert C. Martin’s proposed approach: surround coding agents with unit and acceptance tests, QA procedures, coverage, mutation testing, and other constraints, then use rapid feedback on predictions to expose defects. The article’s sharper warning is that human code review was never foolproof either, leaving responsibility and liability unresolved.