On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories

2026-04-27

AI Summary

This study presents the first systematic quantification of the impact of review bots on pull requests (PRs) generated by AI agents, examining how feedback quality—assessed in terms of relevance, clarity, and conciseness—and comment volume relate to PR acceptance rates and resolution times. Leveraging the AI_Dev dataset comprising 4,532 PRs and 7,416 comments, the authors employ natural language processing and statistical analysis to model comment themes, tone, prescriptiveness, and semantic relevance. Findings reveal that bot-generated comments predominantly address bug fixes, testing, and documentation, exhibiting a consistently polite and instructive tone. However, higher comment volume correlates with reduced average feedback relevance and longer resolution times, while feedback quality itself shows no statistically significant association with PR outcomes.

Technology Category

• Intelligent Robots: Embodied AI
• Natural Language Processing: Safety and Robustness
• Humans and AI: User Experience and Usability

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Autonomous coding agents are reshaping software development by creating pull requests (PRs) on GitHub, referred to as agentic PRs. In parallel, the review process is also becoming autonomous, thereby making reviewer bots key actors in the assessment of these agentic PRs. However, their influence on PR acceptance and resolution remains unclear. This study empirically investigates the relationship between reviewer-bot feedback and PR outcomes by analyzing how Reviewer Bot Feedback Quality (relevance, clarity, conciseness) and Reviewer Bot Activity Volume (comment count) are associated with PR acceptance and resolution time. We analyze 7,416 reviewer-bot comments on 4,532 PRs from the AI_Dev dataset (a dataset that captured AI agents' PRs in GitHub projects). Our results show that reviewer-bot comments mainly focus on bug fixes, testing, and documentation, are civil in tone, and are prescriptive in nature. Reviewer bots generally produce clear and concise feedback, though the semantic relevance of comments to underlying code changes is moderate. We find that higher Reviewer Bot Activity volume is associated with longer PR resolution times and lower average feedback quality, showing that as bots generate more comments on a PR, the average pertinence of that feedback appears to degrade. At the same time, Reviewer Bot Feedback Quality shows no meaningful association with workflow outcomes. Our findings suggest that, in agentic PR workflows, reviewer bots should prioritize targeted high-relevance feedback over generating large numbers of comments.

Problem

• reviewer bots
• agentic pull requests
• feedback quality
• PR resolution time
• GitHub

Innovation

• reviewer bots
• agentic pull requests
• feedback quality
• automated code review
• AI in software development