Facilitating User-Centric Model-Based Systems Engineering Using Generative AI

Elias Bader, Dominik Vereno, Christian Neureiter

2024

Abstract

The increasing complexity of cyber-physical systems requires model-based systems engineering (MBSE) in an effort to sustain a comprehensive oversight. However, broader adaptation of these models requires specialized knowledge and training. In order to make this process more user-friendly, the concept of user-centric systems engineering emerged. Artificial intelligence (AI) could help users overcome beginner hurdles and leverage their contribution quality. This research investigates the feasibility of a large language model in the systems engineering context, with a particular emphasis on the identification of potential obstacles for similar tasks. Therefore, a GPT model is trained on a dataset consisting of UML component diagram elements. In conclusion, the promising results of this research justify utilizing AI in MBSE. Complex relationships between the UML elements were not only understood, they were also generated using natural-language text. Problems arise from the extensive nature of the XMI, the context limitation and the unique identifiers of the UML elements. The fine-tuning process enabled the LLM to gain valuable insights into UML modeling while transferring their base knowledge, which is a promising step toward reducing complexity in MBSE.

Paper Citation

in Harvard Style

Bader E., Vereno D. and Neureiter C. (2024). Facilitating User-Centric Model-Based Systems Engineering Using Generative AI. In Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration; ISBN 978-989-758-682-8, SciTePress, pages 371-377. DOI: 10.5220/0012623200003645

in Bibtex Style

@conference{mbse-ai integration24,
author={Elias Bader and Dominik Vereno and Christian Neureiter},
title={Facilitating User-Centric Model-Based Systems Engineering Using Generative AI},
booktitle={Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration},
year={2024},
pages={371-377},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012623200003645},
isbn={978-989-758-682-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Model-Based Software and Systems Engineering - Volume 1: MBSE-AI Integration
TI - Facilitating User-Centric Model-Based Systems Engineering Using Generative AI
SN - 978-989-758-682-8
AU - Bader E.
AU - Vereno D.
AU - Neureiter C.
PY - 2024
SP - 371
EP - 377
DO - 10.5220/0012623200003645
PB - SciTePress