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

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

Paper (2020)

An external link is available for this item

Department:	Department of Computer Engineering and Software Engineering
ISBN:	9781728156194
PolyPublie URL:	https://publications.polymtl.ca/46678/
Conference Title:	IEEE International Conference on Software Maintenance and Evolution (ICSME 2020)
Conference Date(s):	2020-09-28 - 2020-10-02
Publisher:	IEEE
DOI:	10.1109/icsme46990.2020.00095
Official URL:	https://doi.org/10.1109/icsme46990.2020.00095
Date Deposited:	18 Apr 2023 15:01
Last Modified:	08 Apr 2025 12:24

Cite in APA 7:	Washizaki, H., Takeuchi, H., Khomh, F., Natori, N., Doi, T., & Okuda, S. (2020, September). Practitioners' insights on machine-learning software engineering design patterns: a preliminary study [Paper]. IEEE International Conference on Software Maintenance and Evolution (ICSME 2020). https://doi.org/10.1109/icsme46990.2020.00095
APA 7 (HTML)ASCII CitationAtomBibTeXDOIsDublin CoreEndNote (.ENW)MARC (ISO 2709/UTF-8) (.MRC)MARC (UTF-8) (.MRK8)MARC XMLMPEG-21 DIDLMultiline CSVOAI-ORE Resource Map (Atom Format)OPENAIREOpenURL ContextObjectOpenURL ContextObject in SpanRDF+N-TriplesRDF+N3RDF+XMLReferReference Manager (.RIS)Simple Metadata

Statistics

Repository Staff Only