Recently, using Large Language Models (LLMs) to generate optimization models from natural language descriptions has became increasingly popular. However, a major open question is how to validate that the generated models are correct and satisfy the requirements defined in the natural language description. In this work, we propose a novel agent-based method for automatic validation of optimization models that builds upon and extends methods from software testing to address optimization modeling . This method consists of several agents that initially generate a problem-level testing API, then generate tests utilizing this API, and, lastly, generate mutations specific to the optimization model (a well-known software testing technique assessing the fault detection power of the test suite). In this work, we detail this validation framework and show, through experiments, the high quality of validation provided by this agent ensemble in terms of the well-known software testing measure called mutation coverage.

翻译：近年来，利用大型语言模型（LLMs）从自然语言描述生成优化模型已日益普及。然而，一个主要的开放性问题是如何验证生成的模型是否正确并满足自然语言描述中定义的需求。在本研究中，我们提出了一种新颖的基于代理的优化模型自动验证方法，该方法基于软件测试方法并扩展至优化建模领域。该方法包含多个代理：首先生成问题级别的测试API，随后利用该API生成测试用例，最后生成针对优化模型的变异（一种著名的软件测试技术，用于评估测试套件的故障检测能力）。本研究详细阐述了该验证框架，并通过实验证明，该代理集合在验证质量方面表现优异，具体以软件测试中著名的变异覆盖率指标衡量。