Predicting Acceptance and Review Effort in Human and Agent Pull Requests

• URL: http://arxiv.org/abs/2607.12057v1
• Date: Mon, 13 Jul 2026 18:18:27 GMT
• Title: Predicting Acceptance and Review Effort in Human and Agent Pull Requests
• Abstract summary: Pull requests (PRs) are a central mechanism for reviewing and integrating code changes in modern software repositories.<n>This paper studies whether outcomes can be estimated at the time a PR is opened, before reviewer discussion, CI feedback, or merge decisions are available.<n>We construct a leakage-aware prediction pipeline for human- and agent-authored PRs.
• Score: 6.709069732781736
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Pull requests (PRs) are a central mechanism for reviewing and integrating code changes in modern software repositories. As AI coding agents begin to submit more code changes alongside human developers, maintainers face a new challenge: deciding which PRs are likely to be accepted and which ones may require substantial review effort. This paper studies whether such outcomes can be estimated at the time a PR is opened, before reviewer discussion, CI feedback, or merge decisions are available. Using the AIDev dataset, we construct a leakage-aware prediction pipeline for human- and agent-authored PRs. The feature set is limited to submission-time information, including PR text characteristics, metadata, repository context, temporal signals, and lightweight diff statistics. We evaluate classical machine-learning models, including Logistic Regression, Random Forests, Gradient Boosting, Extra Trees, and MLPs, across pooled, human-only, agent-only, and balanced contributor views. Our results show that acceptance prediction is feasible from early signals: tree-based models achieve F1 scores above 0.95, with textual clarity and metadata among the most influential predictors. Review-effort prediction is more difficult. Comment counts and time-to-merge are only modestly explained by submission-time features, suggesting that reviewer availability, project workflow, and team-specific review practices play a major role. These findings indicate that early PR models can support triage and reviewer prioritization, but should be used as advisory tools rather than automated decision-makers.

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