Practitioners' insights on machine-learning software engineering design patterns: A preliminary study

Hironori Washizaki*, Hironori Takeuchi, Foutse Khomh, Naotake Natori, Takuo Doi, Satoshi Okuda

*この研究の対応する著者

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Machine-learning (ML) software engineering design patterns encapsulate reusable solutions to commonly occurring problems within the given contexts of ML systems and software design. These ML patterns should help develop and maintain ML systems and software from the design perspective. However, to the best of our knowledge, there is no study on the practitioners' insights on the use of ML patterns for design of their ML systems and software. Herein we report the preliminary results of a literature review and a questionnaire-based survey on ML system developers' state-of-practices with concrete ML patterns.

本文言語English
ホスト出版物のタイトルProceedings - 2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ797-799
ページ数3
ISBN(電子版)9781728156194
DOI
出版ステータスPublished - 2020 9月
イベント36th IEEE International Conference on Software Maintenance and Evolution, ICSME 2020 - Virtual, Adelaide, Australia
継続期間: 2020 9月 272020 10月 3

出版物シリーズ

名前Proceedings - 2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020

Conference

Conference36th IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
国/地域Australia
CityVirtual, Adelaide
Period20/9/2720/10/3

ASJC Scopus subject areas

  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理
  • モデリングとシミュレーション

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