Search papers, labs, and topics across Lattice.
This paper introduces an LLM-driven methodology to address the interoperability challenges between diverse modeling tools in the automotive sector, focusing on the mapping of model instances to a target metamodel and the merging of metamodels. By applying this approach to Ecore and SysML v2 metamodels, the authors demonstrate that large language models can automate model transformations while ensuring structural validation against user-defined targets. The findings reveal a substantial reduction in manual transformation effort, highlighting the potential for enhanced cross-tool interoperability in Model-Driven Engineering.
Large language models can automate complex model transformations in automotive engineering, cutting manual effort significantly while ensuring structural validity.
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.
