SWE-Review：借助代理式代码审查实现问题解决的闭环

摘要

编码代理越来越多地为真实世界的软件问题生成拉取请求（PR），但一次性PR生成仍为开环过程：PR被提出后缺乏系统性的审查、诊断或修订。我们提出SWE-Review框架，通过代理型代码审查实现闭环。给定一个问题和一个AI生成的PR，审查代理将探索代码仓库，决定该PR是否应被接受，并提供结构化的修订反馈。我们使用所提出的SWE-Review-Bench评估这一设定，以衡量审查正确性与下游修订的有效性。此外，我们整理了SWE-Review-Traj数据集，用于研究代理型审查的更广泛应用，并填补开放审查训练中的数据稀缺缺口。实验表明，代理型审查通过“生成-审查-修订”循环持续改进PR，在决策准确性和修订后解决率方面均优于单轮固定上下文的审查，其效果可迁移至改进问题解决模型，并实现高效且有效的测试时扩展。这些结果将代理型代码审查定位为一种实用机制，推动AI编码代理从一次性PR生成迈向闭环问题解决。

English

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.