从以人为中心的代码评审到代理式代码评审：不同代际的生成式人工智能技术对评审质量的影响

摘要

代码审查有助于在代码集成前维护软件质量，但也给人类审查者带来了沉重的工作负担。随着生成式人工智能成为软件开发的一部分，代码审查正从主要依赖人类审查的过程转向人工智能支持的审查过程，其中大语言模型（LLM）审查者和AI代理审查者与人类审查者共同参与。然而，我们仍缺乏关于这一转变如何影响审查效率和审查质量的实证证据。本文研究了来自207个GitHub项目的102万个已审查拉取请求，这些项目经历了三个代码审查时代的转变：以人为中心的审查、LLM辅助审查和代理式代码审查。我们识别出三种AI审查者采用实践：渐进式AI采用、快速LLM采用和快速AI代理采用。我们进一步将拉取请求审查讨论建模为审查者交互序列，以描述人类、LLM和AI代理审查者在审查过程中的协作方式。结果表明，涉及代理的协作模式——尤其是由AI代理发起或涉及多个AI代理的审查——在渐进式AI采用和快速AI代理采用模式下与更快的审查决策相关。然而，这些效率提升并未转化为更好的审查质量。我们还发现，审查活动和拉取请求类型在各个时代仍然重要，而一旦LLM和AI代理审查者参与其中，人机协作模式便成为审查效率最强的解释因素。这些发现为设计既能提高效率又不削弱审查质量的人工智能支持代码审查流程提供了实证指导。

English

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.