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

被引：7

作者：

Washizaki, Hironori ^{[1
]}

Takeuchi, Hironori ^{[2
]}

Khomh, Foutse ^{[3
]}

Natori, Naotake ^{[4
]}

Doi, Takuo ^{[5
]}

Okuda, Satoshi ^{[6
]}

机构：

[1] Waseda Univ, Tokyo, Japan

[2] Musashi Univ, Tokyo, Japan

[3] Polytech Montreal, Montreal, PQ, Canada

[4] AISIN SEIKI Co Ltd, Kariya, Aichi, Japan

[5] Lifematics Inc, Tokyo, Japan

[6] Japan Adv Inst Sci & Technol, Nomi, Ishikawa, Japan

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE AND EVOLUTION (ICSME 2020) | 2020年

关键词：

Machine Learning; Design Patterns; Systematic Literature Review; Questionnaire Survey;

D O I：

10.1109/ICSME46990.2020.00095

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：797 / 799

页数：3

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