AI Code Review Study: Faster but Not Better
QueensUniversity · hf · 2026-07-20
This study analyzes 1.02 million code reviews (Pull Requests) across 207 GitHub projects, exploring the evolution across three phases: from "manual review" to "LLM-assisted review" and finally "agentic review".
Key Findings:
- Faster Reviews: Introducing AI agents (especially multi-Agent collaborations or Agent-initiated reviews) significantly accelerates review decision speeds.
- No Quality Improvement: This efficiency boost does not translate into higher code review quality.
- Paradigm Shift: Once LLMs and AI Agents are involved, human-AI collaboration becomes the strongest factor explaining review efficiency, lessening the importance of traditional variables like code activity types.
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