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<p>Model-driven engineering (MDE) has seen significant advancements with the integration of machine learning (ML) and deep learning techniques. Building upon the groundwork of previous investigations, our study provides a concise overview of current large language models (LLMs) applications in MDE, emphasizing their role in automating tasks like model repository classification and developing advanced recommender systems. The paper also outlines the technical considerations for seamlessly integrating LLMs in MDE, offering a practical guide for researchers and practitioners. Looking forward, the paper proposes a focused research agenda for the future interplay of LLMs and MDE, identifying key challenges and opportunities. This concise roadmap envisions the deployment of LLM techniques to enhance the management, exploration, and evolution of modeling ecosystems. Moreover, we also discuss the adoption of LLMs in various domains by means of model-driven techniques and tools, i.e., MDE for supporting LLMs. By offering a compact exploration of LLMs in MDE, this paper contributes to the ongoing evolution of MDE practices, providing a forward-looking perspective on the transformative role of large language models in software engineering and model-driven practices.</p>

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On the use of large language models in model-driven engineering

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Juri Di Rocco,
  • Davide Di Ruscio,
  • Claudio Di Sipio,
  • Phuong T. Nguyen,
  • Riccardo Rubei

摘要

Model-driven engineering (MDE) has seen significant advancements with the integration of machine learning (ML) and deep learning techniques. Building upon the groundwork of previous investigations, our study provides a concise overview of current large language models (LLMs) applications in MDE, emphasizing their role in automating tasks like model repository classification and developing advanced recommender systems. The paper also outlines the technical considerations for seamlessly integrating LLMs in MDE, offering a practical guide for researchers and practitioners. Looking forward, the paper proposes a focused research agenda for the future interplay of LLMs and MDE, identifying key challenges and opportunities. This concise roadmap envisions the deployment of LLM techniques to enhance the management, exploration, and evolution of modeling ecosystems. Moreover, we also discuss the adoption of LLMs in various domains by means of model-driven techniques and tools, i.e., MDE for supporting LLMs. By offering a compact exploration of LLMs in MDE, this paper contributes to the ongoing evolution of MDE practices, providing a forward-looking perspective on the transformative role of large language models in software engineering and model-driven practices.