Studying Software Engineering Patterns for Designing Machine Learning Systems

Hironori Washizaki, Hiromu Uchida, Foutse Khomh, Yann Gaël Guéhéneuc

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

Machine-learning (ML) techniques are becoming more prevalent. ML techniques rely on mathematics and software engineering. Researchers and practitioners studying best practices strive to design ML systems and software that address software complexity and quality issues. Such design practices are often formalized as architecture and design patterns by encapsulating reusable solutions to common problems within given contexts. However, a systematic study to collect, classify, and discuss these software-engineering (SE) design patterns for ML techniques have yet to be reported. Our research collects good/bad SE design patterns for ML techniques to provide developers with a comprehensive classification of such patterns. Herein we report the preliminary results of a systematic-literature review (SLR) of good/bad design patterns for ML.

本文言語English
ホスト出版物のタイトルProceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ49-54
ページ数6
ISBN(電子版)9781728155906
DOI
出版ステータスPublished - 2019 12
イベント10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019 - Tokyo, Japan
継続期間: 2019 12 132019 12 14

出版物シリーズ

名前Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019

Conference

Conference10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
CountryJapan
CityTokyo
Period19/12/1319/12/14

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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