# Repo2Run: Automated Building Executable Environment for Code Repository at Scale

arXiv: https://arxiv.org/abs/2502.13681 · HTML: https://mlsanity.com/paper/2502.13681/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 30 of 30)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models](https://mlsanity.com/paper/2310.04406/) (2023)
- [Training Software Engineering Agents and Verifiers with SWE-Gym](https://mlsanity.com/paper/2412.21139/) (2024)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://mlsanity.com/paper/2405.17238/) (2024)
- [SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution](https://mlsanity.com/paper/2501.05040/) (2025)
- [EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories](https://mlsanity.com/paper/2404.00599/) (2024)
- [MarsCode Agent: AI-native Automated Bug Fixing](https://mlsanity.com/paper/2409.00899/) (2024)
- [Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://mlsanity.com/paper/2212.10561/) (2022)
- [ToolCoder: Teach Code Generation Models to use API search tools](https://mlsanity.com/paper/2305.04032/) (2023)
- [ReposVul: A Repository-Level High-Quality Vulnerability Dataset](https://mlsanity.com/paper/2401.13169/) (2024)
- [AXNav: Replaying Accessibility Tests from Natural Language](https://mlsanity.com/paper/2310.02424/) (2023)
- [Flows: Building Blocks of Reasoning and Collaborating AI](https://mlsanity.com/paper/2308.01285/) (2023)
- [VulEval: Towards Repository-Level Evaluation of Software Vulnerability Detection](https://mlsanity.com/paper/2404.15596/) (2024)
- [Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing](https://mlsanity.com/paper/2311.08649/) (2023)
- [DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippets](https://mlsanity.com/paper/1905.11127/) (2019)
- [Static Code Analysis in the AI Era: An In-depth Exploration of the Concept, Function, and Potential of Intelligent Code Analysis Agents](https://mlsanity.com/paper/2310.08837/) (2023)
- [Aligning the Objective of LLM-based Program Repair](https://mlsanity.com/paper/2404.08877/) (2024)
- [RepoMasterEval: Evaluating Code Completion via Real-World Repositories](https://mlsanity.com/paper/2408.03519/) (2024)
- [XUAT-Copilot: Multi-Agent Collaborative System for Automated User Acceptance Testing with Large Language Model](https://mlsanity.com/paper/2401.02705/) (2024)
- [Automatically Generating Dockerfiles via Deep Learning: Challenges and Promises](https://mlsanity.com/paper/2303.15990/) (2023)
- [Less is More? An Empirical Study on Configuration Issues in Python PyPI Ecosystem](https://mlsanity.com/paper/2310.12598/) (2023)

## Built on by (most cited first, 30 of 39)

- [Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories](https://mlsanity.com/paper/2506.18824/) (2025)
- [ProgramBench: Can Language Models Rebuild Programs From Scratch?](https://mlsanity.com/paper/2605.03546/) (2026)
- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [SWE-Universe: Scale Real-World Verifiable Environments to Millions](https://mlsanity.com/paper/2602.02361/) (2026)
- [Can Language Models Discover Scaling Laws?](https://mlsanity.com/paper/2507.21184/) (2025)
- [Failure as a Process: An Anatomy of CLI Coding Agent Trajectories](https://mlsanity.com/paper/2607.09510/) (2026)
- [From Question Answering to Task Completion: A Survey on Agent System and Harness Design](https://mlsanity.com/paper/2606.20683/) (2026)
- [SERA: Soft-Verified Efficient Repository Agents](https://mlsanity.com/paper/2601.20789/) (2026)
- [SWE-Next: Scalable Real-World Software Engineering Tasks for Agents](https://mlsanity.com/paper/2603.20691/) (2026)
- [EvoConfig: Self-Evolving Multi-Agent Systems for Efficient Autonomous Environment Configuration](https://mlsanity.com/paper/2601.16489/) (2026)
- [DeployBench: Benchmarking LLM Agents for Research Artifact Deployment](https://mlsanity.com/paper/2606.05238/) (2026)
- [Scalable Supervision for Software Agents via Patch Reasoning](https://mlsanity.com/paper/2510.22775/) (2025)
- [SWE-MiniSandbox: Container-Free Reinforcement Learning for Building Software Engineering Agents](https://mlsanity.com/paper/2602.11210/) (2026)
- [Benchmarking LLMs for Fine-Grained Code Review with Enriched Context in Practice](https://mlsanity.com/paper/2511.07017/) (2025)
- [MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering](https://mlsanity.com/paper/2601.22859/) (2026)
- [ChainSWE: Benchmarking Coding Agents on Multi-Bug Software Maintenance](https://mlsanity.com/paper/2607.02606/) (2026)
- [DockSmith: Scaling Reliable Coding Environments via an Agentic Docker Builder](https://mlsanity.com/paper/2602.00592/) (2026)
- [ResearchEnvBench: Benchmarking Agents on Environment Synthesis for Research Code Execution](https://mlsanity.com/paper/2603.06739/) (2026)
- [Repo0: Design-Driven Zero-to-All Code Generation](https://mlsanity.com/paper/2608.19854/) (2026)
- [PaperRepro: Automated Computational Reproducibility Assessment for Social Science Papers](https://mlsanity.com/paper/2603.00058/) (2026)
- [Self-Evolving Coding Agents](https://mlsanity.com/paper/2608.03392/) (2026)
- [SkillForge: Self-Distilling Agents for Project-Specific Issue Resolution](https://mlsanity.com/paper/2608.18933/) (2026)
- [HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing](https://mlsanity.com/paper/2509.23835/) (2025)
- [LLMs as Packagers of HPC Software](https://mlsanity.com/paper/2511.05626/) (2025)
- [TimeMachine-bench: A Benchmark for Evaluating Model Capabilities in Repository-Level Migration Tasks](https://mlsanity.com/paper/2601.22597/) (2026)
- [MEMRES: A Memory-Augmented Resolver with Confidence Cascade for Agentic Python Dependency Resolution](https://mlsanity.com/paper/2604.16941/) (2026)
- [RAT: RunAnyThing via Fully Automated Environment Configuration](https://mlsanity.com/paper/2604.23190/) (2026)
- [SetupX: Can LLM Agents Learn from Past Failures in Functionality-Correct Code Repository Setup?](https://mlsanity.com/paper/2605.26186/) (2026)
- [Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments](https://mlsanity.com/paper/2607.28591/) (2026)
- [PIPer: On-Device Environment Setup via Online Reinforcement Learning](https://mlsanity.com/paper/2509.25455/) (2025)

## Abstract (arXiv)

Scaling up executable code data is significant for improving language models' software engineering capability. The intricate nature of the process makes it labor-intensive, time-consuming and expert-knowledge-dependent to build a large number of executable code repositories, limiting the scalability of existing work based on running tests. The primary bottleneck lies in the automated building of test environments for different repositories, which is an essential yet underexplored task. To mitigate the gap, we introduce Repo2Run, the first LLM-based agent aiming at automating the building of executable test environments for any repositories at scale. Specifically, given a code repository, Repo2Run iteratively builds the Docker image, runs unit tests based on the feedback of the building, and synthesizes the Dockerfile until the entire pipeline is executed successfully. The resulting Dockerfile can then be used to create Docker container environments for running code and tests. We created a benchmark containing 420 Python repositories with unit tests for evaluation. The results illustrate that Repo2Run achieves an 86.0% success rate, outperforming SWE-agent by 77.0%. The resources of Repo2Run are available at https://github.com/bytedance/Repo2Run.