Meta's JiTTests generates failing tests to catch bugs pre-merge
Rather than accelerating code review, the system shifts AI verification to proving behavior changes with executable tests, a mechanism competitors like GitHub Copilot and CodeRabbit do not yet automate end-to-end. Teams running large-scale code review pipelines can adapt the weak-to-strong capture filtering and LLM judge panels to control false-positive noise. Meta's figures are self-reported without independent controls, so treat the 60% true-positive rate and 20% incident reduction as directional benchmarks. Engineering leads evaluating pre-merge defect detection should study the two-workflow design.
