TY - GEN
T1 - Studying Software Engineering Patterns for Designing Machine Learning Systems
AU - Washizaki, Hironori
AU - Uchida, Hiromu
AU - Khomh, Foutse
AU - Guéhéneuc, Yann Gaël
N1 - Funding Information:
Yasuhiro Watanabe and Prof. Kazunori Sakamoto for their helps. This work was supported by JST-Mirai Program Grant Number JP18077318, Japan.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Anti-patterns
KW - Architecture Patterns
KW - Design Patterns
KW - ML Patterns
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85078118145&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078118145&partnerID=8YFLogxK
U2 - 10.1109/IWESEP49350.2019.00017
DO - 10.1109/IWESEP49350.2019.00017
M3 - Conference contribution
AN - SCOPUS:85078118145
T3 - Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
SP - 49
EP - 54
BT - Proceedings - 2019 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Workshop on Empirical Software Engineering in Practice, IWESEP 2019
Y2 - 13 December 2019 through 14 December 2019
ER -