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

Understand

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.