RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms

Abstract: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 been adopted by several recent works on agentic benchmarking and training.

Software Engineering (cs.SE); Machine Learning (cs.LG); Multiagent Systems (cs.MA)

arXiv:2603.05026 [cs.SE]

(or arXiv:2603.05026v3 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2603.05026 Focus to learn more arXiv-issued DOI via DataCite

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