arXiv:2604.23190v2 Announce Type: replace-cross Abstract: Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of configuring executable environments. However, manual configuration remains a labor-intensive bottleneck, necessitating a transition toward fully automated environment configuration. Existing approaches often rely on pre-defined artifacts or are restricted to specific programming languages, limiting their applicability to diverse real-world repositories. In this paper, we first propose RAT (RunAn
The rapid advancement of large language models and autonomous agents necessitates a solution for automated environment configuration to unlock their full potential in complex software engineering tasks.
Fully automated environment configuration addresses a critical bottleneck for autonomous code agents, enabling them to tackle repository-level software development with greater efficiency and applicability across diverse real-world projects.
The shift from manual and language-specific environment setups to a fully automated and generalizable approach will significantly expand the scope and capability of AI agents in software development.
- · AI agent developers
- · Software development companies
- · DevOps platforms
- · Large language model providers
- · Manual environment configuration specialists
- · Companies reliant on specific tooling ecosystems
Automated environment configuration makes autonomous code agents more powerful and versatile for software engineering.
Increased adoption of AI agents could lead to significant reductions in software development cycle times and costs.
The democratization of advanced software engineering capabilities through AI agents might reshape the global competitive landscape for tech innovation.
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Read at arXiv cs.AI