Since the emergence of GPT-3, Large Language Models (LLMs) have caught the eyes of researchers, practitioners, and educators in the field of software engineering. However, there has been relatively little investigation regarding the performance of LLMs in assisting with requirements analysis and UML modeling. This paper explores how LLMs can assist novice analysts in creating three types of typical UML models: use case models, class diagrams, and sequence diagrams. For this purpose, we designed the modeling tasks of these three UML models for 45 undergraduate students who participated in a requirements modeling course, with the help of LLMs. By analyzing their project reports, we found that LLMs can assist undergraduate students as novice analysts in UML modeling tasks, but LLMs also have shortcomings and limitations that should be considered when using them.

翻译：自GPT-3问世以来，大型语言模型（LLMs）已引起软件工程领域研究人员、从业者和教育工作者的广泛关注。然而，关于LLMs在辅助需求分析与UML建模方面的性能表现，目前仍缺乏深入研究。本文探讨了LLMs如何协助初学者分析师创建三种典型UML模型：用例模型、类图和序列图。为此，我们为参与需求建模课程的45名本科生设计了这三种UML模型的建模任务，并在LLMs的辅助下展开研究。通过分析学生的项目报告，我们发现LLMs能够协助作为初学者的本科生完成UML建模任务，但LLMs也存在若干缺陷与局限性，在使用时需予以充分考虑。