# From Human-Centric to Agentic Code Review: The Impact of Different Generations of Generative AI Technology on Review Quality

arXiv: https://arxiv.org/abs/2607.13196 · HTML: https://mlsanity.com/paper/2607.13196/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 59 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 11 of 11)

- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [AI-powered Code Review with LLMs: Early Results](https://mlsanity.com/paper/2404.18496/) (2024)
- [Rethinking Code Review Workflows with LLM Assistance: An Empirical Study](https://mlsanity.com/paper/2505.16339/) (2025)
- [Does AI Code Review Lead to Code Changes? A Case Study of GitHub Actions](https://mlsanity.com/paper/2508.18771/) (2025)
- [Developer-LLM Conversations: An Empirical Study of Interactions and Generated Code Quality](https://mlsanity.com/paper/2509.10402/) (2025)
- [Identifying Unmaintained Projects in GitHub](https://mlsanity.com/paper/1809.04041/) (2018)
- [Detecting Refactoring Commits in Machine Learning Python Projects: A Machine Learning-Based Approach](https://mlsanity.com/paper/2404.06572/) (2024)
- [An Empirical Analysis of Community and Coding Patterns in OSS4SG vs. Conventional OSS](https://mlsanity.com/paper/2601.03430/) (2026)
- [Human-AI Synergy in Agentic Code Review](https://mlsanity.com/paper/2603.15911/) (2026)

## Abstract (arXiv)

Code review helps maintain software quality before code integration, but it also imposes a substantial workload on human reviewers. As generative artificial intelligence becomes part of software development, code review is shifting from a primarily human review process toward AI-supported review processes in which large language model (LLM) reviewers and AI agent reviewers participate alongside human reviewers. However, we still lack empirical evidence on how this transition affects review efficiency and review quality. In this paper, we study 1.02 million reviewed pull requests from 207 GitHub projects that transition across three code review eras: human-centric review, LLM-assisted review, and agentic code review. We identify three AI reviewer adoption practices: Gradual AI Adoption, Rapid LLM Adoption, and Rapid AI Agent Adoption. We further model pull request review discussions as reviewer interaction sequences to characterize how human, LLM, and AI agent reviewers collaborate during the review process. Our results show that agent-involved collaboration patterns, especially reviews initiated by AI agents or involving multiple AI agents, are associated with faster review decisions under Gradual AI Adoption and Rapid AI Agent Adoption. However, these efficiency gains do not translate into better review quality. We also find that review activity and pull request type remain important across eras, while human-AI collaboration patterns become the strongest explanatory factor for review efficiency once LLM and AI agent reviewers participate. These findings provide empirical guidance for designing AI-supported code review processes that improve efficiency without weakening review quality.