What is software quality for AI engineers? : towards a thinning of the fog
Golendukhina, Valentina; Lenarduzzi, Valentina; Felderer, Michael (2022-10-17)
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
© 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.
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https://urn.fi/URN:NBN:fi-fe2023032733348
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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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