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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems, MODELS 2022
Subtitle of host publicationCompanion Proceedings
PublisherAssociation for Computing Machinery, Inc
Pages77-78
Number of pages2
ISBN (Electronic)9781450394673
DOIs
Publication statusPublished - 2022 Oct 23
Event25th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022 - Montreal, Canada
Duration: 2022 Oct 232022 Oct 28

Publication series

NameProceedings - 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
Country/TerritoryCanada
CityMontreal
Period22/10/2322/10/28

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Software

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