SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review

• URL: http://arxiv.org/abs/2607.06065v1
• Date: Tue, 07 Jul 2026 09:37:45 GMT
• Title: SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review
• Abstract summary: Coding agents increasingly generate pull requests (PRs) for real-world software issues.<n>One-shot PR generation remains open-loop: the PR is proposed without systematic review, diagnosis, or revision.<n>We introduce textbfSWE-Review, a framework for closing this loop with agentic code review.
• Score: 47.72392142544407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coding agents increasingly generate pull requests (PRs) for real-world software issues, yet one-shot PR generation remains open-loop: the PR is proposed without systematic review, diagnosis, or revision. We introduce \textbf{SWE-Review}, a framework for closing this loop with agentic code review. Given an issue and an AI-generated PR, a reviewer agent explores the repository, decides whether the PR should be accepted, and provides structured feedback for revision. We evaluate this setting with our proposed \textbf{SWE-Review-Bench} to measure both review correctness and downstream revision usefulness. We further curate \textbf{SWE-Review-Traj} dataset to study broader applications of agentic review and fill the data-scarcity gap for open reviewer training. Experiments show that agentic review continuously improves PRs through a generate-review-revise loop, outperforms single-turn fixed-context review in both decision accuracy and resolve rate after revision, transfers beyond review to improve issue-resolution models, and enables effective and efficient test-time scaling. These results position agentic code review as a practical mechanism for moving AI coding agents from one-shot PR generation toward closed-loop issue resolution.

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