The AIQ Meta-Testbed : Pragmatically Bridging Academic AI Testing and Industrial Q Needs
(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:
https://lup.lub.lu.se/record/43af8feb-4fd4-49af-bd9f-b7d706854f5b
- author
- Borg, Markus LU
- organization
- publishing date
- 2021
- 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
- 1865-1348
- 1865-1356
- 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
- 2024-09-08 06:28:34
@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 = {{1865-1348}}, 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}}, }