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Authors: Jati Husen 1 ; 2 ; Hironori Washizaki 1 ; Nobukazu Yoshioka 1 ; Hnin Tun 1 ; Yoshiaki Fukazawa 1 and Hironori Takeuchi 3

Affiliations: 1 Waseda University, Tokyo, Japan ; 2 Telkom University, Bandung, Indonesia ; 3 Musashi University, Tokyo, Japan

Keyword(s): Machine Learning, Consistency Check, Metamodel, Multi-View.

Abstract: Machine Learning systems provide different challenges for their development. However, machine learning systems also require specific attention toward traditional aspects of software engineering. This situation often leads developers to use different models to cover different views that need to be handled during machine learning system development which often leads to conflicting information between models. In this research, we developed a multi-view modeling framework for machine learning system development by utilizing a metamodel as its backbone for consistency. We have conducted a case study and controlled experiment to evaluate the framework. In conclusion, our framework does help manage the consistency between different views but relies heavily on the quality of available support tools.

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Paper citation in several formats:
Husen, J.; Washizaki, H.; Yoshioka, N.; Tun, H.; Fukazawa, Y. and Takeuchi, H. (2023). Metamodel-Based Multi-View Modeling Framework for Machine Learning Systems. In Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - MODELSWARD; ISBN 978-989-758-633-0; ISSN 2184-4348, SciTePress, pages 194-201. DOI: 10.5220/0011699600003402

@conference{modelsward23,
author={Jati Husen. and Hironori Washizaki. and Nobukazu Yoshioka. and Hnin Tun. and Yoshiaki Fukazawa. and Hironori Takeuchi.},
title={Metamodel-Based Multi-View Modeling Framework for Machine Learning Systems},
booktitle={Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - MODELSWARD},
year={2023},
pages={194-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011699600003402},
isbn={978-989-758-633-0},
issn={2184-4348},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering - MODELSWARD
TI - Metamodel-Based Multi-View Modeling Framework for Machine Learning Systems
SN - 978-989-758-633-0
IS - 2184-4348
AU - Husen, J.
AU - Washizaki, H.
AU - Yoshioka, N.
AU - Tun, H.
AU - Fukazawa, Y.
AU - Takeuchi, H.
PY - 2023
SP - 194
EP - 201
DO - 10.5220/0011699600003402
PB - SciTePress

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