Plausible Is Not Enough: Trust and Evidence in AI-Authored Pull Request Review

Abstract

Code review is where AI-authored pull requests (AIPRs) must earn reviewer trust before integration, yet it is unclear whether reviewers apply the same criteria used for human-authored pull requests (HPRs). We surveyed 239 practitioners from 31 countries using matched HPR and AIPR questions to compare trust, evidence requirements, and verification practices within respondents. Most respondents (64.9%) reported lower initial trust in AIPRs. They relied less on author track record and subsystem familiarity, emphasizing instead human prompter expertise and bounded change size. For approval, respondents more often considered their own reasoning through critical cases sufficient for AIPRs than HPRs (55.2% versus 28.5%), while relying less on test coverage. Overall, 61.1% reported higher evidence requirements for AIPRs, and the share who would block approval under residual uncertainty increased from 19.4% for HPRs to 43.9% for AIPRs. Respondents prioritized test inspection, project context, and local execution, and more often selected partial reimplementation to verify non-trivial logic. Unclear ownership was also more likely to pause AIPR review (29.3% versus 14.6%). Open-ended responses pointed to the difficulty of validating plausible code without reliable context and human accountability. These findings frame AIPR review as an evidence-ownership problem, not merely a correctness check. Reviewers need to connect generated changes to validated behavior and responsible maintainers. Review tools and agentic workflows should expose provenance, distinguish generated claims from human-validated evidence, and make uncertainty visible before approval.