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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages797-799
Number of pages3
ISBN (Electronic)9781728156194
DOIs
Publication statusPublished - 2020 Sep
Event36th IEEE International Conference on Software Maintenance and Evolution, ICSME 2020 - Virtual, Adelaide, Australia
Duration: 2020 Sep 272020 Oct 3

Publication series

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

Conference

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

Keywords

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

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Modelling and Simulation

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