AI 写代码跨仓库就抓瞎
现在的 AI 程序员评测都在一个代码仓库里玩，但真实软件生态里，一个功能或 bug 修复常常要同时改好几个仓库。这篇论文造了第一套跨仓库任务集：从 103 个真实软件生态里挖出 120 个任务，一半修 bug、一半加功能。最强配置(Codex CLI 配 GPT-5.6)成功率也只有 42.5%，大多数时候 AI 要么根本没找到该改哪个仓库，要么找到了却改不完。更关键的是，让 AI 一个仓库一个仓库地单独改，效果反而更差——它只会补上漏掉的部分，却不会修正改错的地方；同时看多个仓库，反而能借助关联信息把活干对。这不是你明天能用的工具，但它划了一条线：AI 写代码的下一道坎，不是单点能力，而是跨仓库的全局协调。
📄 原文摘要(英文)
Coding-agent evaluation has progressed from resolving individual issues to carrying out long-horizon development, yet task completion is still largely assessed within a single codebase. In software ecosystems, many features and bug fixes require coordinated changes across multiple repositories. We introduce WideSWE to evaluate coding agents on such cross-repository tasks. Mining and reviewing changes across 103 software ecosystems yields 120 real-world tasks, balanced between 60 bug fixes and 60 features. We derive prompts from related issues and pull requests. We systematically review and adapt hidden tests to support diverse correct implementations while preserving required behavior and regression checks. Across seven agent configurations, full task success ranges from 10.83% to 42.50%, with the configuration pairing Codex CLI with GPT-5.6-sol achieving the highest rate. Trajectories show agents failing to identify necessary changes, recognizing changes but leaving them unfinished, or modifying the required repositories without fully satisfying the request. To examine whether working on one repository at a time can alleviate these difficulties, we compare it with joint execution under identical prompts. Independent execution mainly recovers omitted work and is less effective at correcting previously attempted but unsuccessful implementations. Joint execution can use information from related repositories to guide implementation and verification. Code is available at https://github.com/ZJU-ACES-ISE/WideSWE.