SWE-World: Building Software Engineering Agents in Docker-Free Environments

2026-02-03

AI Summary

This work proposes SWE-World, a novel framework that eliminates the need for physical Docker containers in software engineering agent training. Traditional approaches rely on containerized execution to obtain feedback, incurring substantial resource overhead and limiting scalability. In contrast, SWE-World leverages large language models to construct a simulated environment that predicts intermediate execution states and test outcomes based on real-world interaction data. This enables supervised fine-tuning (SFT), reinforcement learning (RL), and test-time multi-trajectory selection (TTS) entirely within a virtual setting. Evaluated on SWE-bench Verified, the framework boosts the pass rate of Qwen2.5-Coder-32B from 6.2% to 68.2%, demonstrating its effectiveness and scalability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedback from containerized environments, which require dependency-complete setup and physical execution of programs and tests. While effective, this paradigm is resource-intensive and difficult to maintain, substantially complicating agent training and limiting scalability. We propose SWE-World, a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents. SWE-World leverages LLM-based models trained on real agent-environment interaction data to predict intermediate execution outcomes and final test feedback, enabling agents to learn without interacting with physical containerized environments. This design preserves the standard agent-environment interaction loop while eliminating the need for costly environment construction and maintenance during agent optimization and evaluation. Furthermore, because SWE-World can simulate the final evaluation outcomes of candidate trajectories without real submission, it enables selecting the best solution among multiple test-time attempts, thereby facilitating effective test-time scaling (TTS) in software engineering tasks. Experiments on SWE-bench Verified demonstrate that SWE-World raises Qwen2.5-Coder-32B from 6.2\% to 52.0\% via Docker-free SFT, 55.0\% with Docker-free RL, and 68.2\% with further TTS. The code is available at https://github.com/RUCAIBox/SWE-World

Problem

• software engineering agents
• containerized environments
• execution feedback
• scalability
• resource-intensive

Innovation

• Docker-free
• surrogate environment
• software engineering agents
• test-time scaling
• LLM-based simulation