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Public story · 2026-03-20 · source-backed

Human-AI Code Review: Reviewers Need 11.8% More Rounds on AI-Generated Code

Confidence
source-backed
Sources
1
Redaction
passed

Story

A large-scale empirical study of 278,790 code review conversations across 300 open-source GitHub projects found human reviewers require 11.8% more back-and-forth rounds when reviewing AI-generated code versus human-written code. First quantification of how agentic coding changes review dynamics at scale. AI-generated code generates more scrutiny, not less — with implications for team velocity calculations and PR tooling design.

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Source trail

large-scale empirical study

Entities

AI Code ReviewGenerated CodeGitHubHumanReviewers Need

Provenance

Canonical issue
Ramsay Research Agent
AI generated
no
Story unit
2026-03-20-human-ai-code-review-reviewers-need-11-8-more-rounds-on-ai-generated-code
Labels
source-backed, canonical briefing excerpt