LLM-based Iterative Approach to Metamodeling in Automotive

• URL: http://arxiv.org/abs/2503.05449v1
• Date: Fri, 07 Mar 2025 14:19:17 GMT
• Title: LLM-based Iterative Approach to Metamodeling in Automotive
• Authors: Nenad Petrovic, Fengjunjie Pan, Vahid Zolfaghari, Alois Knoll,
• Abstract summary: This paper introduces an automated approach to domain-specific metamodel construction relying on Large Language Model (LLM)<n>A prototype was implemented as web service using Python programming language, while OpenAI's GPT-4o was used as the underlying LLM.
• Score: 3.7311118301529125
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we introduce an automated approach to domain-specific metamodel construction relying on Large Language Model (LLM). The main focus is adoption in automotive domain. As outcome, a prototype was implemented as web service using Python programming language, while OpenAI's GPT-4o was used as the underlying LLM. Based on the initial experiments, this approach successfully constructs Ecore metamodel based on set of automotive requirements and visualizes it making use of PlantUML notation, so human experts can provide feedback in order to refine the result. Finally, locally deployable solution is also considered, including the limitations and additional steps required.

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