What is Software Quality for AI Engineers? Towards a Thinning of the Fog
It addresses software quality challenges for AI engineers in SMEs, but is incremental as it builds on existing SQA concepts.
The study investigated software quality assurance strategies in AI/ML component development through interviews with ten Austrian SMEs, identifying 12 quality issues and their detection methods.
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.