Assisting Stakeholders in Class Diagram Interpretation with LLMs: a Work in Progress

Diagrams can be valuable tools in requirements engineering to establish a shared understanding between software engineers and stakeholders. However, interacting with these visual representations can be challenging for some stakeholders who prefer textual descriptions and may need support to interpret notation elements and understand the diagram structure and meaning. To address this need, we explore the use of Large Language Models to effectively assist stakeholders interacting with diagrams by providing automatic textual explanations and contextual guidance. Specifically, we aim to design and evaluate with stakeholders an interactive layer (integrated into an end-user-oriented modelling tool) that provides automatic diagram explanations in natural language. As a first step toward our research objective, this paper investigates the capability of GPT4 to generate appropriate textual descriptions from domain models. We use a test data set consisting of UML class diagrams in various formats, belonging to the domain of digital agriculture, and develop a set of prompts to generate the interactive explanatory layer. We conduct a technical evaluation of the output, focusing on correctness, completeness, and understandability. The results provide valuable insights to inform future design and research, while also revealing potential challenges in real-world applications.