自动化跨语言跨平台代码库构建与管理，成功率提升至78%。
RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms
- 基于语言模型代理，自动处理依赖、编译和测试
- 在多语言多系统环境下实现78%构建成功率
- 适合需要大规模自动化软件工程数据集的研究者
语言模型代理已推动自动化软件工程显著进展，但大规模构建与测试代码仓库仍主要依赖人工，效率低下。本文提出RepoLaunch，一种新型智能体框架，可自动处理跨多种编程语言和操作系统环境的依赖解析、源码编译及测试结果提取。实验表明，RepoLaunch在多语言多平台场景下达到78%的构建成功率，较仅支持Python/Linux的先前系统提升18%。为展示其应用价值，我们构建了完全自动化的软件工程数据集生成流水线，仅需在任务设计阶段进行人工干预。RepoLaunch已开源，其自动化任务生成流程已被多个近期关于智能体基准测试与训练的工作采用。
原文摘要 · Abstract (English)
Language model (LM) agents have driven substantial progress in automated software engineering (SWE), yet building and testing software repositories at scale remains a largely manual and labor-intensive bottleneck. In this work, we introduce RepoLaunch, a novel agentic framework that automatically resolves dependencies, compiles source code, and extracts test results across diverse programming languages and operating systems. RepoLaunch achieves a 78% build success rate, outperforming the Python/Linux-only prior system by 18%. To demonstrate its application, we further present a fully automated pipeline for SWE dataset creation driven by RepoLaunch, which only requires human input at the task-design stage. RepoLaunch is open-sourced, and its automated task-generation pipeline has already been adopted by several recent works on agentic benchmarking and training.
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