Large language models should serve as translators in modeling and simulation workflows, coordinated with specialized tools and supported by LoRA-based task adapters on a shared backbone.
On the Utility of Domain Modeling Assistance with Large Language Models
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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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cs.SE 1years
2025 1verdicts
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From over-reliance to smart integration: using Large-Language Models as translators between specialized modeling and simulation tools
Large language models should serve as translators in modeling and simulation workflows, coordinated with specialized tools and supported by LoRA-based task adapters on a shared backbone.