On the Utility of Domain Modeling Assistance with Large Language Models

• URL: http://arxiv.org/abs/2410.12577v1
• Date: Wed, 16 Oct 2024 13:55:34 GMT
• Title: On the Utility of Domain Modeling Assistance with Large Language Models
• Authors: Meriem Ben Chaaben, Lola Burgueño, Istvan David, Houari Sahraoui,
• Abstract summary: 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.
• Score: 2.874893537471256
• License:
• Abstract: 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.

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