On the Utility of Domain Modeling Assistance with Large Language Models
This work addresses the problem of time constraints and incomplete domain understanding in software modeling for software modelers, representing an incremental improvement by applying existing LLM techniques to a specific domain.
The paper tackles the challenges in model-driven engineering by proposing a novel approach using large language models and few-shot prompt learning to assist in domain modeling, resulting in the development of the MAGDA tool and a user study that provides insights into its usability and effectiveness.
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 paper 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 AI-based completion models on scarce 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.