TY - GEN
T1 - Practitioners' insights on machine-learning software engineering design patterns
T2 - 36th IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
AU - Washizaki, Hironori
AU - Takeuchi, Hironori
AU - Khomh, Foutse
AU - Natori, Naotake
AU - Doi, Takuo
AU - Okuda, Satoshi
N1 - Funding Information:
This work was supported by JSPS Bilateral Program JPJSBP120209936, JST-Mirai Program Grant Numbers JP18077318 and JP20319852, and enPiT-Pro Smart SE.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Design Patterns
KW - Machine Learning
KW - Questionnaire Survey
KW - Systematic Literature Review
UR - http://www.scopus.com/inward/record.url?scp=85096722289&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096722289&partnerID=8YFLogxK
U2 - 10.1109/ICSME46990.2020.00095
DO - 10.1109/ICSME46990.2020.00095
M3 - Conference contribution
AN - SCOPUS:85096722289
T3 - Proceedings - 2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
SP - 797
EP - 799
BT - Proceedings - 2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 September 2020 through 3 October 2020
ER -