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Interpreting cloud computer vision pain-points: a mining study of stack overflow

Cummaudo, Alex, Vasa, Rajesh, Barnett, Scott, Grundy, John and Abdelrazek, Mohamed 2020, Interpreting cloud computer vision pain-points: a mining study of stack overflow, in ICSE 2020 : Proceedings of the 2020 ACM/IEEE 42nd International Conference on Software Engineering, IEEE, Piscataway, N.J., pp. 1584-1596, doi: 10.1145/3377811.3380404.

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Title Interpreting cloud computer vision pain-points: a mining study of stack overflow
Author(s) Cummaudo, AlexORCID iD for Cummaudo, Alex orcid.org/0000-0001-7878-6283
Vasa, RajeshORCID iD for Vasa, Rajesh orcid.org/0000-0003-4805-1467
Barnett, ScottORCID iD for Barnett, Scott orcid.org/0000-0002-3187-4937
Grundy, John
Abdelrazek, MohamedORCID iD for Abdelrazek, Mohamed orcid.org/0000-0003-3812-9785
Conference name Software engineering. International conference (42nd : 2020 : Online from Seoul, South Korea)
Conference location Seoul, South Korea
Conference dates 2020/06/27 - 2020/07/19
Title of proceedings ICSE 2020 : Proceedings of the 2020 ACM/IEEE 42nd International Conference on Software Engineering
Editor(s) [Unknown]
Publication date 2020
Start page 1584
End page 1596
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) intelligent services
computer vision
documentation
pain points
stack overflow
empirical study
CORE2020 A*
Notes This conference was originally scheduled to be held in Seoul, South Korea, however due the 2020 Covid Pandemic, it was held online
ISBN 9781450371216
ISSN 0270-5257
Language eng
DOI 10.1145/3377811.3380404
Indigenous content off
HERDC Research category E1 Full written paper - refereed
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145254

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.