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ReviewDebt: a practical framework for scoring every pull request — Sachin Gupta, Ebay
ReviewDebt scores PR verification burden from deterministic repository signals, not inferred authorship. Track its weekly slope to see whether agent-driven output is exceeding review capacity.
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Source Summary
ReviewDebt combines **five signal families** and ten deterministic checks into a 0–100 score. A scan of **524 PRs across three public repositories** estimated **228 senior-reviewer hours**, while AI indicators appeared on 5–20% of weekly PRs without dominating the high-burden band.
Practical Implication
Backfill the score over recent merged PRs, calibrate weights against actual reviewer experience and plot the weekly slope. Use it to demand tests, rationale or smaller changes before scarce senior review time is assigned.
Agent-Ready Context
ReviewDebt combines **five signal families** and ten deterministic checks into a 0–100 score. A scan of **524 PRs across three public repositories** estimated **228 senior-reviewer hours**, while AI indicators appeared on 5–20% of weekly PRs without dominating the high-burden band. Backfill the score over recent merged PRs, calibrate weights against actual reviewer experience and plot the weekly slope. Use it to demand tests, rationale or smaller changes before scarce senior review time is assigned. The score estimates burden rather than correctness, and its default weights need local calibration. Authorship indicators are informational; the reported scan suggests structural complexity and PR volume, not AI attribution alone, drove the largest burdens.
Context Map
benchmarkcoding#agent-evals#agent-reliability#coding-agentsUncertainty
The score estimates burden rather than correctness, and its default weights need local calibration. Authorship indicators are informational; the reported scan suggests structural complexity and PR volume, not AI attribution alone, drove the largest burdens.