  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.
