Practitioners’ insights on machine-learning software engineering design patterns: a preliminary study
- Resource Type
- Conference
- Authors
- Washizaki, Hironori; Takeuchi, Hironori; Khomh, Foutse; Natori, Naotake; Doi, Takuo; Okuda, Satoshi
- Source
- 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) ICSME Software Maintenance and Evolution (ICSME), 2020 IEEE International Conference on. :797-799 Sep, 2020
- Subject
- Computing and Processing
Software maintenance
Software design
Conferences
Bibliographies
Machine learning
Software engineering
Machine Learning
Design Patterns
Systematic Literature Review
Questionnaire Survey
- Language
- ISSN
- 2576-3148
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.