在GitHub宇宙中的沉浸：将编码代理扩展至精通
Immersion in the GitHub Universe: Scaling Coding Agents to Mastery
AI总结：
本文提出ScaleSWE系统，通过自动化多代理工作流生成高质量SWE数据，产生10万验证实例，超越现有数据集的多样性和复杂性，训练出性能提升三倍的ScaleSWE代理。
摘要：
在现实世界软件工程任务中，实现精通的根本瓶颈在于大规模高质量训练数据的稀缺性。此类数据的扩展受到环境设置复杂性、单元测试生成和问题陈述编纂的限制。本文提出ScaleSWE，一种自动化、沙盒化的多代理工作流，旨在大规模构建高质量SWE数据。系统协调三个专门的代理进行环境设置、测试创建和问题描述合成，处理5200个仓库中的600万次拉取请求，生成Scale SWE数据：10万验证SWE实例，目前最大此类数据集。它在仓库多样性和真实任务复杂性上显著超越现有现实世界数据集。我们进一步展示了数据集的训练用途，通过提炼71498高质量轨迹并微调Qwen30BA3BInstruct生成ScaleSWE代理。我们的代理在SWE Bench Verified上达到64的解决率，比基模型提高了近三倍。ScaleSWE提供了一种可扩展、可重复的数据构建方法，以推进基于LLM的软件工程。Scale SWE将公开可用。
英文摘要：
Achieving mastery in real world software engineering tasks is fundamentally bottlenecked by the scarcity of large scale, high quality training data. Scaling such data has been limited by the complexity of environment setup, unit test generation, and problem statement curation. In this paper, we propose ScaleSWE, an automated, sandboxed multi agent workflow designed to construct high quality SWE data at scale. The system coordinates three specialized agents for environment setup, test creation, and problem description synthesis to process 6 million pull requests across 5200 repositories, producing Scale SWE Data: 100k verified SWE instances, the largest such dataset to date. It substantially surpasses existing real world datasets in repository diversity and reflects realistic task complexity. We further demonstrate the dataset utility for training by distilling 71498 high quality trajectories and finetuning Qwen30BA3BInstruct to produce ScaleSWE Agent. Our agent achieves a 64 resolve rate on SWE Bench Verified a nearly three fold improvement over the base model. ScaleSWE provides a scalable, reproducible approach for data construction to advance LLM based software engineering. Scale SWE will be publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
