ReviewDebt: a practical framework for scoring every pull request — Sachin Gupta, Ebay
Jul 12, 2026 · 25:00
Sachin Gupta introduces ReviewDebt, a deterministic framework scoring every pull request on the hidden review burden it creates, arguing that coding agents produce code faster than humans can responsibly review, accumulating an unmeasured debt. He defines 5 signal families—diff size and coupling, test evidence gap, directory and ownership spread, AI authorship indicators, and evidence and rational gaps—with 10 deterministic checks computable from PR data without LLMs, to produce a 0–100 score. Real PR examples show a clean human PR scoring 0, a high-debt AI PR scoring 60 with 86 estimated review minutes, and a well-shaped AI PR scoring 7. A cross-repo scan of 524 PRs over 27–90 days found 228 senior reviewer hours accumulated, with 5–20% of PRs weekly firing AI indicators but none of those landing in the high-burden band—complexity, not authorship, drives burden. Gupta advises teams to backfill the scorer on last 200 PRs, set a threshold (e.g., 50), surface scores as PR comments, aggregate weekly per team, and bring the number to retrospectives, turning vibe into measurement for 2027's governance conversation.
