ReviewDebt: a practical framework for scoring every pull request — Sachin Gupta, Ebay

2.0K views · Jul 12, 2026 · 25:00 min · Watch on YouTube ↗

Takeaway

Measure the verification burden of generated changes alongside coding speed to keep review debt visible.

Summary

Review debt is the gap between code agents produce and code humans have reviewed, trusted, and understood.
The speaker argues that PR throughput and merge speed can conceal reviewer fatigue, architectural drift, and weak tests.
ReviewDebt uses ten deterministic checks across diff size and coupling, test evidence, ownership spread, AI authorship, and rationale gaps.
Test-to-production-line ratios indicate whether tests exist but cannot establish their behavioral quality; broad ownership increases coordination cost.

review-debtpull-requestscode-quality

Original description

Coding agents ship PRs faster than humans can trust them. The gap is filling up with a debt nobody is measuring — and it's about to swallow your engineering velocity.
Every team in 2026 measures coding agents the same way: PR count, lines of code, cycle time, developer NPS. None of those see the real cost — bloated diffs, weak tests, ambiguous rationale, ownership sprawl, and human reviewers spending more time verifying AI code than they used to spend writing their own.
This talk introduces ReviewDebt: a practical framework for scoring every pull request on the hidden review burden it creates. The scoring is deterministic — diff size, test-coverage delta, ownership spread, generated-code smells, evidence and rationale gaps — so the number is defensible in a real engineering review. We'll walk three real PRs side-by-side (clean human PR, high-debt AI PR, refactored AI PR), watch the scoring play out signal by signal, and look at a 90-day dashboard from a production backend org where review debt climbs in lockstep with AI-PR share.

Speakers:
Sachin Gupta: Sachin Gupta is a Staff Software Engineer with 15+ years building backend platforms at internet scale, currently focused on the runtime trust boundaries that LLM coding agents blur and the creator of HeapLens, a Java heap analyzer extension used in 50+ countries.
  LinkedIn:   / guptasachin1  
  GitHub: https://github.com/sachinkg12