RepoLaunch: Automating Build and Management of Code Repositories across Languages and Platforms
arXiv:2603.05026v2 Announce Type: replace-cross 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 demonst
The rapid advancement of large language models (LLMs) has created both the necessity and the capability for automating complex software engineering tasks previously deemed too intricate for machines.
Automating the build and management of software repositories across diverse languages and platforms addresses a significant bottleneck in scaling software development and validation, directly impacting the efficiency of AI-driven software creation and maintenance.
The labor-intensive and manual aspects of software repository management and testing are being incrementally replaced by AI agentic frameworks, accelerating development cycles and reducing human intervention.
- · AI software development platforms
- · Large enterprises with complex software stacks
- · Developers working on multi-language projects
- · Manual software configuration specialists
- · Traditional build engineering tool vendors
- · Organizations slow to adopt AI-driven SWE
Significantly faster and more reliable software integration and deployment across various environments.
Increased pace of innovation in AI applications as the overhead of managing underlying code infrastructure decreases.
Potential for drastically smaller, more agile development teams capable of managing sophisticated, heterogeneous software systems.
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