Model-Driven Engineering (MDE) has advanced significantly with the integration of Machine Learning (ML) and Deep Learning (DL) techniques. To understand the transformative potential of Large Language Models (LLMs) in MDE, we first explore the spectrum of artificial intelligence: from Artificial Narrow Intelligence (ANI), which specializes in specific MDE tasks, to the more versatile Artificial General Intelligence (AGI).

We will look at an overview of current LLM applications in MDE, emphasizing their role in automating tasks such as model repository classification and developing sophisticated recommender systems.

Furthermore, we provide a comprehensive outline of the technical considerations for integrating LLMs into MDE workflows. This practical guide is designed to equip researchers and practitioners with the necessary knowledge to seamlessly implement LLMs into their MDE processes.

Looking ahead, we will propose a targeted research agenda to explore the future interplay of LLMs and MDE, identifying key challenges and opportunities. This roadmap envisions the use of LLM techniques to enhance the management, exploration, and evolution of modeling ecosystems.

Juri Di Rocco is a tenure-track assistant professor in the Department of Information Engineering, Computer Science, and Mathematics at the University of L’Aquila. He earned his PhD from the same institution, working within the MDEGroup research team under the supervision of Alfonso Pierantonio and Davide Di Ruscio.

His research interests encompass all aspects of software language engineering. His primary focus lies in Model Driven Engineering (MDE), specifically in domain-specific modeling languages, recommender systems for MDE, modeling repositories, and mining techniques.