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

Description and use

Build a 10‑bug calibration set, run a free AI reviewer, measure recall and precision, and decide whether to ship, constrain, or retire it.

Problem

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

Outcome

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

Workflow

• 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.

Evidence

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

Source

Recall = true positives ÷ 10 injected bugs

Source