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arXiv Machine Learning · 2026/7/30 17:44:31

Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments

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最新研究给AI程序员打造了一座"练习题库"!这项技术把程序员在真实项目中提交的代码改动,自动转化为带标准答案的编程任务,让AI能在接近实战的环境中反复训练。以前准备训练数据要耗费大量人力手工搭建环境,现在这个系统能从1,130个真实代码改动中自动产出近八成可用的任务,效率大幅提升,还节省了重复搭建环境的成本。 通俗地说,就像给学厨的徒弟配备了一套"还原名厨菜品"的练习题:每道题都来自真实餐厅的招牌菜,食材、工具、火候标准全部配齐,做完还能自动批改。这套系统覆盖了修bug、加新功能、写测试、升级接口和修复安全漏洞五类常见编程任务,既解决了AI训练数据不足的燃眉之急,又保证了练习内容与真实工作场景高度一致。 这项进展的直接影响是让AI编程助手变得更聪明可靠。随着AI用更多高质量的真实项目数据训练,未来普通用户使用的编程工具将能更准确地理解需求、更少出错误,软件开发的成本也会进一步下降。这意味着我们常用的APP和网站,新功能的上线速度可能更快,使用体验也会更加稳定流畅。
Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a specification, development tools, and reliable verification. To expand this supply, we present Change2Task, a system grounded in repository history that converts merged pull requests into verified tasks on healthy modern revisions of the same repository. It aligns historical evidence with evolved code, reconstructs task states through Patch Reversal, Code Mapping, or Agent Reconstruction, and validates the lifecycle from a healthy base to a task state and a restored state. By deriving multiple tasks grounded in developer evidence from maintained environments, Change2Task provides executable data for coding agent training and evaluation while reducing repeated environment setup, storage, and task construction effort. We evaluate the system through five common and widely adopted coding agent task families: Bug Fix, Feature Addition, Test Generation, Application Programming Interface Migration, and Security Repair. Starting from 1,130 source changes eligible for construction, Change2Task achieves 79.6% verified task construction success across these task families. On a matched candidate set, it recovers 29.2% more verified tasks than a construction baseline based on pull requests. Historical and reconstructed cases achieve up to 98.0% matched outcome agreement under agent evaluation, while reuse of modern bases reduces measured expenditure across the complete pipeline by 10.8%.
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