用大模型自动解决汽车领域建模工具间的互操作难题
LLM-Driven Approach to Modeling Tool Interoperability in Automotive Domain
- 用大模型实现不同建模语言间的实例映射与元模型合并
- 在汽车案例中验证了生成模型的结构正确性，减少人工工作量
- 适合需要跨工具协作的汽车系统建模人员使用
异构建模工具之间的互操作性仍是模型驱动工程（MDE）中的重大挑战，尤其在汽车领域，多种建模语言及事实标准的专有和开源工具共存。本文提出一种基于大语言模型（LLM）的自动化模型互操作方法，涵盖两个关键方面：1）将模型实例映射到目标元模型；2）元模型合并。该方法通过Ecore与SysML v2元模型之间的转换进行验证，并对生成的模型实例进行用户定义目标模型的结构化校验。汽车领域案例表明，该方法可显著降低手动转换工作量，同时生成符合结构要求的目标模型，实现跨工具互操作。
原文摘要 · Abstract (English)
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.
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