Integrating artificial intelligence (AI) into software engineering can transform traditional practices by enhancing efficiency, accuracy, and innovation. This study explores using ChatGPT, an advanced AI language model, to enhance UML class diagrams dynamically, an underexplored area. Traditionally, creating and maintaining class diagrams are manual, time-consuming, and error-prone processes. This research leverages natural language processing (NLP) techniques to automate the extraction of methods and interactions from detailed use case tables and integrate them into class diagrams. The methodology involves several steps: (1) developing detailed natural language use case tables by master's degree students for a "Waste Recycling Platform," (2) creating an initial static class diagram based on these tables, (3) iteratively enriching the class diagram through ChatGPT integration to analyze use cases and suggest methods, (4) reviewing and incorporating these methods into the class diagram, and (5) dynamically updating the PlantUML \cite{plantuml} class diagram, followed by evaluation and refinement. Findings indicate that the AI-driven approach significantly improves the accuracy and completeness of the class diagram. Additionally, dynamic enhancement aligns well with Agile development practices, facilitating rapid iterations and continuous improvement. Key contributions include demonstrating the feasibility and benefits of integrating AI into software modeling tasks, providing a comprehensive representation of system behaviors and interactions, and highlighting AI's potential to streamline and improve existing software engineering processes. Future research should address identified limitations and explore AI applications in other software models.

翻译：将人工智能（AI）融入软件工程，可以通过提升效率、准确性和创新性来变革传统实践。本研究探讨利用先进的人工智能语言模型ChatGPT来动态增强UML类图，这是一个尚未被充分探索的领域。传统上，创建和维护类图是手动、耗时且易出错的过程。本研究利用自然语言处理（NLP）技术，从详细的用例表中自动提取方法及交互，并将其集成到类图中。该方法包含以下几个步骤：(1) 由硕士研究生为“废物回收平台”开发详细的自然语言用例表，(2) 基于这些用例表创建初始的静态类图，(3) 通过集成ChatGPT迭代地丰富类图，以分析用例并建议方法，(4) 审查这些方法并将其纳入类图，(5) 动态更新PlantUML \cite{plantuml}类图，随后进行评估和优化。研究结果表明，这种AI驱动的方法显著提高了类图的准确性和完整性。此外，动态增强与敏捷开发实践高度契合，有助于快速迭代和持续改进。主要贡献包括：论证了将AI集成到软件建模任务中的可行性和益处，提供了系统行为与交互的全面表示，并凸显了AI在简化和改进现有软件工程流程方面的潜力。未来的研究应解决已识别的局限性，并探索AI在其他软件模型中的应用。