LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain

2026-07-16

AI Summary

This study addresses the interoperability challenges in automotive domain modeling arising from the coexistence of heterogeneous tools, multiple modeling languages, and a mix of proprietary and open-source environments. To tackle this issue, the work proposes a novel automated approach that leverages large language models (LLMs) to map and merge source model instances into target metamodels based on Ecore and SysML v2. A structural validation mechanism is integrated to ensure semantic consistency and syntactic correctness of the generated models. Experimental evaluation on real-world automotive cases demonstrates that the method substantially reduces manual transformation effort while efficiently producing target models that are both structurally valid and aligned with user requirements, thereby establishing a viable new paradigm for cross-tool modeling interoperability.

Abstract

Interoperability between heterogeneous modeling tools remains a significant challenge in Model-Driven Engineering (MDE), particularly in the automotive domain where multiple modeling languages, as well as defacto standard proprietary and open-source tools coexist. This paper presents an LLM-driven approach for automated model interoperability by considering two relevant aspects: 1) mapping model instances to a target metamodel 2) merging of metamodels. The proposed methodology is demonstrated through transformations involving Ecore and SysML v2 based metamodels and incorporates structural validation of generated model instances against user-defined target models. Automotive case studies illustrate the feasibility of the approach and show that large language models can significantly reduce manual transformation effort while generating structurally valid target models for cross-tool interoperability.

Problem

• Model Interoperability
• Model-Driven Engineering
• Automotive Domain
• Metamodel Mapping
• Heterogeneous Modeling Tools

Innovation

• LLM-driven interoperability
• model transformation
• metamodel merging
• structural validation
• automotive MDE