On the use of Large Language Models in Model-Driven Engineering
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913560161943552 |
|---|---|
| author | Di Rocco, Juri Di Ruscio, Davide Di Sipio, Claudio Nguyen, Phuong T. Rubei, Riccardo |
| author_facet | Di Rocco, Juri Di Ruscio, Davide Di Sipio, Claudio Nguyen, Phuong T. Rubei, Riccardo |
| contents | Model-Driven Engineering (MDE) has seen significant advancements with the integration of Machine Learning (ML) and Deep Learning (DL) techniques. Building upon the groundwork of previous investigations, our study provides a concise overview of current Language Large 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. 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 Language Large Models in software engineering and model-driven practices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the use of Large Language Models in Model-Driven Engineering Di Rocco, Juri Di Ruscio, Davide Di Sipio, Claudio Nguyen, Phuong T. Rubei, Riccardo Software Engineering Model-Driven Engineering (MDE) has seen significant advancements with the integration of Machine Learning (ML) and Deep Learning (DL) techniques. Building upon the groundwork of previous investigations, our study provides a concise overview of current Language Large 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. 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 Language Large Models in software engineering and model-driven practices. |
| title | On the use of Large Language Models in Model-Driven Engineering |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2410.17370 |