Modeling tool for managing canvas-based models traceability in ML system development

Jati H. Husen, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki Fukazawa

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
ホスト出版物のサブタイトルCompanion Proceedings
出版社Association for Computing Machinery, Inc
ページ77-78
ページ数2
ISBN(電子版)9781450394673
DOI
出版ステータスPublished - 2022 10月 23
イベント25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022 - Montreal, Canada
継続期間: 2022 10月 232022 10月 28

出版物シリーズ

名前Proceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022: Companion Proceedings

Conference

Conference25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
国/地域Canada
CityMontreal
Period22/10/2322/10/28

ASJC Scopus subject areas

  • 工学(その他)
  • ソフトウェア

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