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.

翻译：语言模型（LM）智能体已推动自动化软件工程（SWE）取得显著进展，然而大规模构建与测试软件仓库仍主要依赖人工操作，成为制约效率的瓶颈。本文提出RepoLaunch这一新型智能体框架，可自动处理跨编程语言与操作系统的依赖解析、源代码编译及测试结果提取。RepoLaunch实现了78%的构建成功率，较仅支持Python/Linux系统的先前方案提升18%。为验证其应用价值，我们进一步展示了基于RepoLaunch的全自动SWE数据集生成流水线——该方案仅在任务设计阶段需要人工输入。RepoLaunch已开源，其自动化任务生成流水线已被近期多项关于智能体基准测试与训练的研究所采用。