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LLMs streamline automotive tool interoperability in new research paper

A new research paper proposes an LLM-driven method to improve interoperability between diverse modeling tools in the automotive sector. The approach focuses on automatically mapping model instances to a target metamodel and merging existing metamodels. This methodology, demonstrated with Ecore and SysML v2, aims to reduce manual effort and ensure structural validity of generated models for cross-tool compatibility. AI

IMPACT Could significantly reduce manual effort in automotive software development by automating model transformations.

RANK_REASON The cluster contains a research paper detailing a new methodology for LLM-driven tool interoperability.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs streamline automotive tool interoperability in new research paper

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The cluster contains a research paper detailing a new methodology for LLM-driven tool interoperability.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Nenad Petrovic, Jiajie Zhang, Vahid Zolfaghari, Alois Knoll ·

    LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain

    arXiv:2607.14659v1 Announce Type: cross 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 propriet…

  2. arXiv cs.AI TIER_1 English(EN) · Alois Knoll ·

    LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain

    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 pres…