SetupX: Can LLM Agents Learn from Past Failures in Functionality-Correct Code Repository Setup?
arXiv:2605.26186v1 Announce Type: cross Abstract: Functionality-correct repository setup aims to configure execution environments (e.g., dependencies, build scripts) to successfully execute a repository's documented features. It presents significant challenges due to diverse, repository-specific failures, including dependency incompatibilities, missing toolchains, incomplete installations, and verification-strategy mismatches. Existing LLM agents struggle to robustly resolve these issues, specifically failing to support (1) cross-repository experience transfer, (2) multi-step trial-and-repair
The rapid advancement of large language models is leading to their application in increasingly complex and autonomous tasks, necessitating agents that can learn from failure.
Improving LLM agents' ability to learn from past failures in complex software environments is critical for their adoption in mission-critical applications and reducing human intervention.
The development of LLM agents capable of cross-repository experience transfer and multi-step trial-and-repair for code setup marks a significant step towards more robust and autonomous AI software development tools.
- · AI software development platforms
- · DevOps engineers leveraging AI
- · LLM developers
- · Companies with large, complex codebases
- · Software maintainers performing repetitive setup tasks
- · Manual debugging services for environmental issues
More efficient and automated setup of complex software projects using AI agents.
Acceleration of software development cycles as AI agents reduce environment configuration bottlenecks.
The development of truly autonomous AI software engineering systems, potentially leading to fully AI-driven codebases.
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Read at arXiv cs.LG