发表机构
University of Saskatchewan（萨斯喀彻温大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过对239名从业者的混合方法调查，明确AI生成拉取请求的审查条件，提出可审查性债务概念，为AIPR治理提供了新视角。
AI 中文摘要
AI编码智能体正从本地代码辅助转向基于拉取的工作流，生成的贡献必须按照现有项目规范进行审查、解释和维护。尽管近期研究已开始刻画AI生成的拉取请求（AIPR），但关于审查者如何管控其进入审查的过程、AI作者身份如何重塑可信度与公平性，以及何种准入机制能保障审查可持续性，人们知之甚少。我们报告了一项混合方法问卷调查，对象为来自31个国家的239名具有代码审查经验且接触过不同程度AIPR的从业者。在调查引出的场景与自我报告中，AI作者身份并非绝对的拒绝信号。相反，受访者表示审查工作量取决于AIPR是否作为负责任的贡献提交，具体条件包括范围有限、基于项目的理由、持续集成（CI）之外的验证、贡献者的响应性以及可识别的合并后所有权。这种条件逻辑也适用于新手AIPR，受访者强调当前审查过程中的可见参与度，而非仅依赖个人资料层面的声誉。定性回应进一步描述了当人工管理难以观察时，指导、审查、延迟和路由方面的变化。我们将缺失的理由、验证和所有权概念化为可审查性债务，即当生成的代码缺乏足够的人工基础时，审查者必须承担的工作。这些发现将AIPR治理重新定义为在生成的贡献消耗稀缺的审查者注意力之前，使人工判断变得可观察。
英文摘要
AI coding agents are moving from local code assistance into pull-based workflows, where generated contributions must be reviewed, explained, and maintained within existing project norms. Although recent work has begun to characterize AI-authored pull requests (AIPRs), less is known about how reviewers govern their entry into review, how AI authorship reshapes credibility and fairness, and what intake mechanisms protect review sustainability. We report a mixed-method questionnaire survey of 239 practitioners from 31 countries with code-review experience and varying exposure to AIPRs. In the scenarios and self-reports elicited by the survey, AI authorship was not a categorical rejection signal. Instead, respondents described review effort as conditional on whether an AIPR arrived as an accountable contribution, with bounded scope, project-grounded rationale, validation beyond Continuous Integration (CI), contributor responsiveness, and identifiable post-merge ownership. This conditional logic extended to newcomer AIPRs, where respondents emphasized visible participation in the current review process over profile-level reputation alone. Qualitative responses further described shifts in mentoring, scrutiny, deferral, and routing when human stewardship was difficult to observe. We conceptualize missing rationale, validation, and ownership as reviewability debt, the work reviewers must absorb when generated code lacks sufficient human grounding. These findings reframe AIPR governance around making human judgment observable before generated contributions consume scarce reviewer attention.
Comments30 pages
