将代码仓库变更自动转为可执行的编程任务，提升训练数据效率。
Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
- 基于仓库历史变更，通过逆向补丁等方法重建任务状态。
- 从1130次变更中成功生成79.6%可验证任务，召回率提升29.2%。
- 适合需真实代码环境的编程智能体训练与评估研究者使用。
扩展编程智能体的训练与评估需要持续提供可执行的数据。每个任务需耦合真实软件状态、规范、开发工具及可靠验证机制。为此，我们提出Change2Task系统，该系统基于仓库历史，将已合并的拉取请求转化为同一仓库健康现代版本上的验证任务。它通过补丁逆向、代码映射或智能体重构技术恢复任务状态，并验证从健康基线到任务状态再到恢复状态的完整生命周期。通过从维护环境中的开发者证据中提取多个任务，Change2Task为编程智能体训练与评估提供可执行数据，同时减少重复的环境搭建、存储与任务构建开销。我们在五类常见且广泛采用的任务类型上进行评估：缺陷修复、功能新增、测试生成、接口迁移和安全修复。从1,130个候选变更中，系统在任务构造上达成79.6%的验证成功率。在匹配候选集上，相比基于拉取请求的基线方法，多恢复29.2%的验证任务。历史与重构案例在智能体评估下最高达98.0%的匹配结果一致率，且复用现代基线使全流程开销降低10.8%。
原文摘要 · Abstract (English)
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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