Modeling tool for managing canvas-based models traceability in ML system development
Authors: 
Jati H. Husen, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki FukazawaAuthors Info & Claims
Jati H. Husen, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki FukazawaAuthors Info & ClaimsPages 77 - 78
Abstract
Analysis of machine learning models often used canvas-based models such as ML Canvas and AI Project Canvas to facilitate rapid brainstorming of ideas. However, those models often cover only high-level descriptions of requirements. Developers may utilize other models to achieve a more comprehensive analysis to cover specific aspects. This condition may lead to inconsistencies between different models. This study proposes a tool to support traceability between canvas-based and other models. The tool is implemented as a plugin for astah* System Safety.
References
[1]
David Adkins, Bilal Alsallakh, Adeel Cheema, Narine Kokhlikyan, Emily McReynolds, Pushkar Mishra, Chavez Procope, Jeremy Sawruk, Erin Wang, and Polina Zvyagina. 2022. Method Cards for Prescriptive Machine-Learning Transparency. In 2022 IEEE/ACM 1st International Conference on AI Engineering - Software Engineering for AI (CAIN). 90--100.
[2]
Louis Dorard. 2015. Machine Learning Canvas. https://www.machinelearningcanvas.com/
[3]
Jati H. Husen, Hnin Thandar Tun, Nobukazu Yoshioka, Hironori Washizaki, and Yoshiaki Fukazawa. 2021. Goal-Oriented Machine Learning-Based Component Development Process. In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C).
[4]
Lukas-Walter Thiée. 2021. A systematic literature review of machine learning canvases. In INFORMATIK 2021. Gesellschaft für Informatik, Bonn, 1221--1235.
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- Modeling tool for managing canvas-based models traceability in ML system development
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October 2022
1003 pages
Copyright © 2022 Owner/Author.
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- Univ. of Montreal: University of Montreal
- IEEE CS
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 November 2022
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- JST-Mirai Program
- JST SPRING
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MODELS '22
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MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems
October 23 - 28, 2022
Quebec, Montreal, Canada
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Overall Acceptance Rate 144 of 506 submissions, 28%
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