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.

翻译：大语言模型（LLM）智能体在软件工程（SWE）领域的发展受到可验证数据集稀缺性的制约，这一瓶颈源于跨不同语言构建可执行环境的复杂性。为此，我们提出MEnvAgent——一个支持可扩展生成可验证任务实例的多语言自动化环境构建框架。MEnvAgent采用多智能体"规划-执行-验证"架构自主解决构建故障，并集成了一种新颖的环境复用机制，通过增量修补历史环境来降低计算开销。在包含10种语言1000个任务的新基准MEnvBench上的评估表明，MEnvAgent优于基线方法，将失败到通过（F2P）率提升8.6%，同时将时间成本降低43%。此外，我们通过构建MEnvData-SWE——迄今为止最大规模的开源多语言真实可验证Docker环境数据集，并附有解决方案轨迹，使各类模型在SWE任务上均能获得一致的性能提升，从而证明了MEnvAgent的实用性。我们的代码、基准和数据集可在https://github.com/ernie-research/MEnvAgent获取。