We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox also integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. MPLSandbox can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of their generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we integrate it into training and deployment approaches, and also employ it to optimize workflows for a wide range of real-world code-related tasks. Our goal is to enhance researcher productivity on LLM-based code-related tasks by simplifying and automating workflows through delegation to MPLSandbox.

翻译：本文介绍MPLSandbox，一个开箱即用的多编程语言沙箱，旨在为大型语言模型（LLMs）提供来自编译器与分析工具的统一且全面的反馈。该系统能够自动识别代码的编程语言，并在隔离的子沙箱中编译与执行代码，以确保安全性与稳定性。此外，MPLSandbox还集成了传统与基于LLM的代码分析工具，可对生成的代码进行全面分析。MPLSandbox能够轻松集成到LLMs的训练与部署流程中，以提升其生成代码的质量与正确性。该系统亦有助于研究人员简化各类基于LLM的代码相关任务的工作流程，降低开发成本。为验证MPLSandbox的有效性，我们将其集成至训练与部署方案中，并应用于优化广泛的实际代码相关任务的工作流程。我们的目标是通过将工作流程委托给MPLSandbox进行简化与自动化，从而提升研究人员在基于LLM的代码相关任务上的工作效率。