SWE-sweep Tests AI Agents Finding Hidden Bugs Without Issue Hints
Researchers from Meta Super Intelligence Lab, Harvard University, Stanford and other institutions introduced SWE-sweep, a software-engineering benchmark built from 100 real open-source repositories containing 4,100 historical vulnerabilities. Unlike conventional issue-driven benchmarks, it gives coding agents only complete repositories and requires them to discover and repair hidden defects without descriptions, error messages or vulnerability locations. DoNews reported that successful repairs must turn a hidden test from failing to passing while preserving the repository’s full original test suite, with the initial leaderboard run uniformly on mini-SWE-agent.
SWE-sweep tests whether coding agents can proactively inspect repositories and find latent defects, a materially different capability from repairing bugs after users provide issue feedback.
(文/杨亮)10月23日消息,近日,来自 Meta 超级智能实验室、哈佛大学、斯坦福等机构的研究者推出全新软件工程评测基准 SWE-sweep,一改主流代码基准的评测范式:不再给 AI 提供问题描述、报错信息或漏洞位置,仅交付完整代码仓库,要求编码智能体自主探索、发现并修复潜藏 Bug。
过往 SWE-bench 等经典基准,任务形式大多是 “给定 Issue 问题描述 + 代码库,让 AI 根据反馈修复漏洞”,相当于提前告诉 AI“哪里出了问题、是什么问题”。而 SWE-sweep 瞄准更贴近真实运维的场景:代码库上线后,在用户上报问题之前,由 AI 主动巡检、挖掘隐性缺陷。
该基准数据集由 100 个真实开源仓库构建,包含合计 4100 个历史漏洞。研究团队筛选特定代码提交版本,让大量历史 Bug 在同一版本共存;同时严格把关样本,只保留 “仅凭阅读代码本身就能发现”的漏洞 —— 缺陷的预期行为可以从代码注释、类型标注、单元测试、函数契约等仓库内信息推导出来,排除需要外部上下文才能够定位的问题。
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