AI BriefingFrom Human-Centric to Agentic Code Review: Faster Decisions Do Not Mean Better Reviews
Key point
Code reviews involving AI Agents have become faster, but this has not led to improved quality.
Details
Researchers from Queen’s University analyzed 1.02 million Pull Requests across 207 GitHub projects to compare the transition from human-centric reviews to LLM-assisted review and Agentic Code Review.
The study classified projects' AI adoption approaches into the following three categories:
- Gradual AI Adoption
- Rapid LLM Adoption
- Rapid AI Agent Adoption
The analysis revealed that patterns where AI Agents initiate reviews or multiple AI Agents participate together were associated with faster review decisions in Gradual AI Adoption and Rapid AI Agent Adoption environments.
However, the increase in review decision speed did not lead to improved review quality. Review activity volume and Pull Request types remained significant factors across all eras, and after the involvement of LLMs and AI Agents, human-AI collaboration patterns emerged as the strongest factor explaining review efficiency.
The researchers suggest that when adopting AI-based code reviews, one should not optimize solely for processing speed, but must also measure and manage both efficiency and quality.
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