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The AIQ Meta-Testbed : Pragmatically Bridging Academic AI Testing and Industrial Q Needs

Borg, Markus LU (2021) 13th Software Quality Days Conference, SWQD 2021 In Lecture Notes in Business Information Processing 404. p.66-77
Abstract

AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.

Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Artificial intelligence, Machine learning, Quality assurance, Software testing, Testbed
host publication
Software Quality : Future Perspectives on Software Engineering Quality - 13th International Conference, SWQD 2021, Proceedings - Future Perspectives on Software Engineering Quality - 13th International Conference, SWQD 2021, Proceedings
series title
Lecture Notes in Business Information Processing
editor
Winkler, Dietmar ; Biffl, Stefan ; Mendez, Daniel ; Wimmer, Manuel and Bergsmann, Johannes
volume
404
pages
12 pages
publisher
Springer Science and Business Media B.V.
conference name
13th Software Quality Days Conference, SWQD 2021
conference location
Vienna, Austria
conference dates
2021-01-19 - 2021-01-21
external identifiers
  • scopus:85101540601
ISSN
1865-1356
1865-1348
ISBN
9783030658533
DOI
10.1007/978-3-030-65854-0_6
language
English
LU publication?
yes
additional info
Funding Information: This work was funded by Plattformen at Campus Helsingborg, Publisher Copyright: © 2021, Springer Nature Switzerland AG.
id
43af8feb-4fd4-49af-bd9f-b7d706854f5b
date added to LUP
2021-12-10 10:35:10
date last changed
2024-06-15 22:26:04
@inproceedings{43af8feb-4fd4-49af-bd9f-b7d706854f5b,
  abstract     = {{<p>AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.</p>}},
  author       = {{Borg, Markus}},
  booktitle    = {{Software Quality : Future Perspectives on Software Engineering Quality - 13th International Conference, SWQD 2021, Proceedings}},
  editor       = {{Winkler, Dietmar and Biffl, Stefan and Mendez, Daniel and Wimmer, Manuel and Bergsmann, Johannes}},
  isbn         = {{9783030658533}},
  issn         = {{1865-1356}},
  keywords     = {{Artificial intelligence; Machine learning; Quality assurance; Software testing; Testbed}},
  language     = {{eng}},
  pages        = {{66--77}},
  publisher    = {{Springer Science and Business Media B.V.}},
  series       = {{Lecture Notes in Business Information Processing}},
  title        = {{The AIQ Meta-Testbed : Pragmatically Bridging Academic AI Testing and Industrial Q Needs}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-65854-0_6}},
  doi          = {{10.1007/978-3-030-65854-0_6}},
  volume       = {{404}},
  year         = {{2021}},
}