| title | SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review |
|---|---|
| authors | Ruoyu Wang, Jierun Chen, Shaowei Wang, Chaofan Tao, Sidi Yang, Yuxin Jiang, Kim-Hui Yap, Lifeng Shang, Xiaohui Li, Haoli Bai |
| date | 2026-07-07 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.06065 |
| affiliation | LegoX |
| upvotes | 10 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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 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 SWE-Review-Bench to measure both review correctness and downstream revision usefulness. We further curate 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. |
cite
@misc{wang2026swe,
title={SWE-Review: Closing the Loop on Issue Resolution with Agentic Code Review},
author={Ruoyu Wang and Jierun Chen and Shaowei Wang and Chaofan Tao and Sidi Yang and Yuxin Jiang and Kim-Hui Yap and Lifeng Shang and Xiaohui Li and Haoli Bai},
year={2026},
eprint={2607.06065},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.06065}
}