MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
Researchers have developed MEnvAgent, a framework designed to automate the creation of executable software engineering environments across multiple programming languages. This system addresses the scarcity of verifiable datasets for training AI agents by employing a Planning-Execution-Verification architecture and an environment reuse mechanism to reduce computational costs. Evaluations on the MEnvBench benchmark showed MEnvAgent improved task completion rates by 8.6% and reduced time costs by 43%, also enabling the creation of the largest open-source polyglot dataset for verifiable Docker environments. AI
IMPACT Enables creation of larger, more realistic datasets for training AI agents in software engineering, potentially improving their capabilities across diverse programming languages.