LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
arXiv:2607.14659
The paper proposes using large language models to automate the mapping and merging of heterogeneous modeling tool metamodels in the automotive domain, enabling cross‑tool model interoperability with reduced manual effort.
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.