🤖 AI Summary
A systematic literature review on the application of large language models (LLMs) to behavioral modeling—particularly automated generation of use case and sequence diagrams—is currently lacking, hindering research consolidation and practical guidance. Method: This paper presents the first comprehensive survey in this domain, identifying 14 core studies via a terminology-driven search strategy and synthesizing prevalent LLM application patterns and evaluation methodologies for behavioral modeling. Results: Findings confirm the feasibility of LLMs for diagram generation tasks; however, existing work is heavily reliant on GPT-series models and largely omits validation by domain experts. The study innovatively advocates for cross-model comparative analysis and expert-in-the-loop evaluation. It thereby provides theoretical foundations and actionable pathways for future research, tool development, and pedagogical practice in model-driven engineering and AI-assisted software modeling.
📝 Abstract
In recent years, large language models (LLMs) have been extensively utilized for behavioral modeling, for example, to automatically generate sequence diagrams. However, no overview of this work has been published yet. Such an overview will help identify future research directions and inform practitioners and educators about the effectiveness of LLMs in assisting behavioral modeling. This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling, particularly focusing on use case and sequence diagrams. Through a term-based search, we filtered and identified 14 relevant primary studies. Our analysis of the selected primary studies reveals that LLMs have demonstrated promising results in automatically generating use case and sequence diagrams. In addition, we found that most of the current literature lacks expert-based evaluations and has mainly used GPT-based models. Therefore, future work should evaluate a broader range of LLMs for behavioral modeling and involve domain experts to evaluate the output of LLMs.