Your AI Reviewer Needs a Baseline: A Zero-Cost Patch Audit Loop

Описание и применение

Автоматизированный цикл аудита AI‑ревью, измеряющий эффективность и стоимость, позволяет небольшим командам принимать обоснованные решения о доверии к авто-ревью.

Задача

Reviewers of AI‑generated code lack measurable metrics, leading to trust gaps and missed defects.

Результат

The audit loop runs on a free server, consumes only free token allowance, and outputs a quantitative score that tells a solo founder when to trust the AI review and when to read the diff alone.

Рабочий процесс

• 1. Prepare a JSON file of historical cases with base and head commits, bug description, and reporter. 2. Run reviewer_audit.py with --repo pointing to the target repo, --cases pointing to the JSON, and --review-cmd specifying the AI review CLI. 3. The script extracts the diff between base and head. 4. It feeds the diff to the review command and captures the output. 5. It judges the output against the ground‑truth case, determining if the bug was caught and if any false positives occurred. 6. Results are aggregated into a score report that shows catch rate, precision, coverage, latency, and token cost. 7. Use the score to decide whether to trust the AI review or manually inspect the diff.

Доказательства

This article walks through a reproducible patch audit loop. It runs on a free server, spends tokens from a free allowance, and produces a score that tells a solo founder when to trust the AI review and when to read the diff alone.

Источник

A useful audit tracks five numbers: Catch rate, Precision, Patch coverage, Latency, Token cost.

Источник