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Valentina Golendukhina, Valentina Lenarduzzi, and Michael Felderer. 2022. What is Software Quality for AI Engineers? Towards a Thinning of the Fog. In 1st Conference on AI Engineering - Software Engineering for AI (CAIN’22), May 16–24, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3522664.3528599

What is software quality for AI engineers? : towards a thinning of the fog

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Author: Golendukhina, Valentina1; Lenarduzzi, Valentina2; Felderer, Michael1
Organizations: 1University of Innsbruck, Innsbruck, Austria
2University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2023032733348
Language: English
Published: Association for Computing Machinery, 2022
Publish Date: 2023-03-27
Description:

Abstract

It is often overseen that AI-enabled systems are also software systems and therefore rely on software quality assurance (SQA). Thus, the goal of this study is to investigate the software quality assurance strategies adopted during the development, integration, and maintenance of AI/ML components and code. We conducted semi-structured interviews with representatives of ten Austrian SMEs that develop AI-enabled systems. A qualitative analysis of the interview data identified 12 issues in the development of AI/ML components. Furthermore, we identified when quality issues arise in AI/ML components and how they are detected. The results of this study should guide future work on software quality assurance processes and techniques for AI/ML components.

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ISBN: 978-1-4503-9275-4
Pages: 1 - 9
DOI: 10.1145/3522664.3528599
OADOI: https://oadoi.org/10.1145/3522664.3528599
Host publication: 2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN)
Conference: International Conference on AI Engineering : Software Engineering for AI
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
AI
Empirical Study
Machine Learning
Software Quality
Article
Funding: This work was partially supported by the Austrian Science Fund(FWF): I 4701-N.
Copyright information: © ACM 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, http://dx.doi.org/10.1145/3522664.3528599.