On the Utility of Domain Modeling Assistance with Large Language Models

This is the authors' abstract. We don't add key points for this paper.

Model-Driven Engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to syntactic constraints hinder the design process. This article presents a study to evaluate the usefulness of a novel approach utilizing Large Language Models (LLMs) and few-shot prompt learning to assist in domain modeling. The aim of this approach is to overcome the need for extensive training of traditional AI-based completion algorithms on domain-specific datasets and to offer versatile support for various modeling activities, providing valuable recommendations to software modelers. To support this approach, we developed MAGDA, a user-friendly tool, through which we conduct a user study and assess the real-world applicability of our approach in the context of domain modeling, offering valuable insights into its usability and effectiveness.