An LLM-based Agent for Reliable Docker Environment Configuration

• URL: http://arxiv.org/abs/2502.13681v2
• Date: Thu, 06 Mar 2025 07:17:09 GMT
• Title: An LLM-based Agent for Reliable Docker Environment Configuration
• Authors: Ruida Hu, Chao Peng, Xinchen Wang, Cuiyun Gao,
• Abstract summary: Repo2Run is an agent designed to fully automate environment configuration and generate executable Dockerfiles for arbitrary Python repositories.<n>We address two major challenges: (1) enabling the LLM agent to configure environments within isolated Docker containers, and (2) ensuring the successful configuration process is recorded and accurately transferred to a Dockerfile without error.<n>We evaluate Repo2Runon our proposed benchmark of 420 recent Python repositories with unit tests, where it achieves an 86.4% success rate, outperforming the best baseline by 63.9%.
• Score: 9.436480907117415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Environment configuration is a critical yet time-consuming step in software development, especially when dealing with unfamiliar code repositories. While Large Language Models (LLMs) demonstrate the potential to accomplish software engineering tasks, existing methods for environment configuration often rely on manual efforts or fragile scripts, leading to inefficiencies and unreliable outcomes. We introduce Repo2Run, the first LLM-based agent designed to fully automate environment configuration and generate executable Dockerfiles for arbitrary Python repositories. We address two major challenges: (1) enabling the LLM agent to configure environments within isolated Docker containers, and (2) ensuring the successful configuration process is recorded and accurately transferred to a Dockerfile without error. To achieve this, we propose atomic configuration synthesis, featuring a dual-environment architecture (internal and external environment) with a rollback mechanism to prevent environment "pollution" from failed commands, guaranteeing atomic execution (execute fully or not at all) and a Dockerfile generator to transfer successful configuration steps into runnable Dockerfiles. We evaluate Repo2Run~on our proposed benchmark of 420 recent Python repositories with unit tests, where it achieves an 86.0% success rate, outperforming the best baseline by 63.9%. Repo2Run is available at https://github.com/bytedance/Repo2Run.

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