What is Software Quality for AI Engineers? Towards a Thinning of the Fog
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 23 Mar 2022.
Builds on
11 of 28 references.
References · 11
- 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 213
- 2020Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructurecited by 138
- 2019A Comprehensive Study on Deep Learning Bug Characteristicscited by 65
- 2018Software Engineering Challenges of Deep Learningcited by 41
- 2020Repairing Deep Neural Networks: Fix Patterns and Challengescited by 33
- 2020Adoption and Effects of Software Engineering Best Practices in Machine Learningcited by 33
- 2021Quality Assurance for AI-based Systems: Overview and Challengescited by 19
- 2020Towards Guidelines for Assessing Qualities of Machine Learning Systemscited by 16
- 2020Software engineering for artificial intelligence and machine learning software: A systematic literature reviewcited by 15
- 2019Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebookscited by 13
- 2020Quality Management of Machine Learning Systemscited by 6
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.