Your AI Reviewer Is Untested: A 10-Bug Calibration Harness on a Free Server

说明与用途

构建10个已知漏洞的校准集，运行免费AI审查器，测量召回率和精确率，决定是否继续使用。

问题

Teams rely on AI reviewers but rarely test their accuracy, risking undetected bugs or false positives in code reviews.

结果

The harness provides concrete recall and precision numbers, turning an untested AI reviewer into a measurable tool and reducing downstream debugging costs.

工作流

• Create a 10‑bug calibration set with known bugs in five Python files.
• Define scoring rules: true positives require matching bug class and line within 3 lines.
• Implement a pure‑Python harness that calls the AI reviewer via its API and collects findings.
• Score the findings against the ground truth to compute recall and precision.
• Interpret the results: high recall but low precision indicates noise; low recall but high precision indicates blindness.
• Decide on action: ship, constrain, or kill the reviewer based on metrics.
• Schedule the harness to run weekly on a free server for continuous monitoring.

证据

A 45‑minute harness turns it into a measurable tool.

来源

Recall = true positives ÷ 10 injected bugs

来源