MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
This addresses a bottleneck in verifiable software engineering for AI researchers and developers, though it is incremental as it builds on existing multi-agent and environment reuse concepts.
The paper tackles the scarcity of verifiable datasets for LLM agents in software engineering by introducing MEnvAgent, a framework for scalable polyglot environment construction, which improves Fail-to-Pass rates by 8.6% and reduces time costs by 43% on a new benchmark.
The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages. To address this, we introduce MEnvAgent, a Multi-language framework for automated Environment construction that facilitates scalable generation of verifiable task instances. MEnvAgent employs a multi-agent Planning-Execution-Verification architecture to autonomously resolve construction failures and integrates a novel Environment Reuse Mechanism that reduces computational overhead by incrementally patching historical environments. Evaluations on MEnvBench, a new benchmark comprising 1,000 tasks across 10 languages, demonstrate that MEnvAgent outperforms baselines, improving Fail-to-Pass (F2P) rates by 8.6% while reducing time costs by 43%. Additionally, we demonstrate the utility of MEnvAgent by constructing MEnvData-SWE, the largest open-source polyglot dataset of realistic verifiable Docker environments to date, alongside solution trajectories that enable consistent performance gains on SWE tasks across a wide range of models. Our code, benchmark, and dataset are available at https://github.com/ernie-research/MEnvAgent.