LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
For automotive engineers and MDE practitioners, this work offers a novel method to automate cross-tool interoperability, reducing manual effort, though it is incremental as it applies existing LLM capabilities to a known bottleneck.
The paper tackles the challenge of modeling tool interoperability in the automotive domain by proposing an LLM-driven approach for automated model transformation and metamodel merging. Results show that the approach significantly reduces manual transformation effort while generating structurally valid target models, as demonstrated through automotive case studies.
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.