Reviewers manage ai authored code requests using accountability and clarity
Who Pays the Review Cost? Triage, Fairness, and Accountability in AI-authored Pull Requests
Software Engineering
Summary
Reviewers of software contributions created by AI do not simply reject them because they come from AI. Instead, the effort put into reviewing depends on whether the AI's work is clear, responsible, and well-explained, like showing why changes were made and who will maintain them. The authors found that if AI contributions lack these qualities, reviewers have to do extra work, which they call "reviewability debt." This research helps understand how human reviewers can better handle AI-generated code while keeping review work fair and manageable.
What this means in practice
- •For software engineering teams: Design review processes that focus on clear rationale, limited scope, and accountable ownership to reduce extra work reviewing AI-generated code.
- •For open source maintainers: Prioritize visible engagement and contribution context in reviewing AI and newcomer pull requests to support fairness and sustainable maintenance.
A survey. It maps existing work.
Authors
Md Shamimur Rahman, Khairul Alam, Banani Roy, Chanchal K. Roy
Abstract
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.