MEnvAgent: New Framework for Building Verifiable Software Engineering Environments Across Multiple Programming Languages
Researchers introduced MEnvAgent, a framework that automatically constructs executable environments for software engineering tasks across 10 programming languages, addressing a major bottleneck in training AI agents. The system uses a multi-agent architecture with an environment reuse mechanism to reduce computational costs while improving task success rates by 8.6%. This work enables creation of larger, more diverse datasets for training and evaluating language model-based software engineering agents.
MEnvAgent is a multi-language framework designed to automate the construction of verifiable environments for software engineering tasks, tackling the scarcity of diverse, executable datasets needed to train Large Language Model agents. The system employs a Planning-Execution-Verification architecture where multiple agents work together to autonomously resolve environment construction failures, and introduces an Environment Reuse Mechanism that incrementally patches historical environments to reduce computational overhead. Evaluation on MEnvBench—a new benchmark with 1,000 tasks spanning 10 programming languages—shows MEnvAgent outperforms baseline approaches with an 8.6% improvement in Fail-to-Pass rates and 43% reduction in time costs. The researchers also constructed MEnvData-SWE, described as the largest open-source polyglot dataset of realistic verifiable Docker environments, which demonstrates consistent performance gains across multiple LLM models on software engineering tasks. Code, benchmark, and dataset are made publicly available.
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- arXiv cs.AICenter
MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
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