Enhancing Class Diagram Dynamics: A Natural Language Approach with ChatGPT
Résumé
Abstract The present research introduces an innovative methodology, assisted by artificial intelligence, aimed at enhancing the dynamics of class diagrams within the field of software engineering. This methodology employs advanced language models, such as ChatGPT, to address the limitations inherent in traditional manual methods, particularly when dealing with complex systems. The approach is iterative, analyzing natural language use cases in detail and extracting key insights through the language understanding capabilities of ChatGPT. These insights are then integrated into a UML class diagram, resulting in demonstrable improvements in both accuracy and completeness. The updated diagram, supplemented with explicit methods derived from use cases, offers a more precise delineation of functional responsibilities and improved class relationships. This leads to a more comprehensive understanding of system interactions. This methodology, which is both versatile and efficient, aligns with UML best practices and holds significant value for Agile development. While further evaluation is necessary, preliminary findings suggest that AI-driven approaches hold considerable potential for enhancing the dynamics of class diagrams and advancing software development practices.
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