從以人為中心到代理型程式碼審查：不同世代生成式AI技術對審查品質的影響

摘要

程式碼審查有助於在程式碼整合前維護軟體品質，但同時也為人工審查者帶來沉重的工作負擔。隨著生成式人工智慧成為軟體開發的一部分，程式碼審查正從以人類為主的審查流程，轉向由大型語言模型（LLM）審查者與AI代理審查者共同參與的AI輔助審查流程。然而，我們仍缺乏關於此轉變如何影響審查效率與審查品質的實證證據。在本論文中，我們研究了來自207個GitHub專案的102萬份已審查拉取請求，這些專案經歷了三個程式碼審查時代：以人類為中心的審查、LLM輔助審查，以及代理式程式碼審查。我們識別出三種AI審查者的採用模式：逐步AI採用、快速LLM採用，以及快速AI代理採用。我們進一步將拉取請求審查討論建模為審查者互動序列，以描述人類、LLM與AI代理審查者在審查過程中如何協作。結果顯示，涉及AI代理的協作模式，特別是那些由AI代理發起或涉及多位AI代理的審查，在逐步AI採用與快速AI代理採用下，與更快速的審查決策相關聯。然而，這些效率提升並未轉化為更好的審查品質。我們也發現，審查活動與拉取請求類型在各個時代仍屬重要因素，而一旦LLM與AI代理審查者參與後，人機協作模式便成為解釋審查效率的最強因子。這些發現為設計既能提升效率又不削弱審查品質的AI輔助程式碼審查流程提供了實證指引。

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.