LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain

2026-07-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

An LLM-driven approach addresses the significant challenge of interoperability between heterogeneous modeling tools in Model-Driven Engineering, particularly within the automotive domain where diverse modeling languages and proprietary or open-source tools coexist. This methodology focuses on two key aspects: mapping model instances to a target metamodel and merging metamodels. The approach is demonstrated using transformations involving Ecore and SysML v2 based metamodels, incorporating structural validation of generated model instances against user-defined target models. Automotive case studies confirm the feasibility of this LLM-driven solution, indicating it can substantially reduce manual transformation effort while producing structurally valid target models for cross-tool interoperability.

Key takeaway

For Automotive Software Engineers struggling with heterogeneous modeling tool interoperability, you should explore LLM-driven solutions to automate model transformations. This approach can significantly reduce manual effort in mapping model instances and merging metamodels, ensuring structurally valid outputs. Consider piloting LLM integration for Ecore and SysML v2 transformations to streamline your development workflows and accelerate cross-tool integration.

Key insights

LLMs automate complex model interoperability in MDE, reducing manual effort and ensuring structural validity.

Principles

• LLMs can map model instances to target metamodels.
• LLMs can merge heterogeneous metamodels.
• Structural validation ensures model integrity.

Method

An LLM-driven procedure maps model instances to a target metamodel and merges metamodels, incorporating structural validation of generated models against user-defined targets.

In practice

• Automate Ecore to SysML v2 model transformations.
• Integrate diverse automotive modeling tools.
• Validate generated models for structural correctness.

Topics

• LLM Applications
• Model Interoperability
• Automotive Software
• Model-Driven Engineering
• Metamodel Transformation
• SysML v2

Best for: AI Scientist, Research Scientist, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.