2022/03/30 by Qunying Song, Markus Borg, Song, Qunying +7
Computer Science · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Software Testing and Debugging Techniques #cs.AI #cs.SE
paper · pdf · doi:10.48550/arxiv.2203.16225
Accepted for publication in the Proc. of CAIN 2022 - 1st International Conference on AI Engineering - Software Engineering for AI
arxiv created 2022/03/30 · openalex publication_date 2022/03/30 · arxiv updated 2022/03/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
There is a growing interest in industry and academia in machine learning (ML) testing. We believe that industry and academia need to learn together to produce rigorous and relevant knowledge. In this study, we initiate a collaboration between stakeholders from one case company, one research institute, and one university. To establish a common view of the problem domain, we applied an interactive rapid review of the state of the art. Four researchers from Lund University and RISE Research Institutes and four practitioners from Axis Communications reviewed a set of 180 primary studies on ML testing. We developed a taxonomy for the communication around ML testing challenges and results and identified a list of 12 review questions relevant for Axis Communications. The three most important questions (data testing, metrics for assessment, and test generation) were mapped to the literature, and an in-depth analysis of the 35 primary studies matching the most important question (data testing) was made. A final set of the five best matches were analysed and we reflect on the criteria for applicability and relevance for the industry. The taxonomies are helpful for communication but not final. Furthermore, there was no perfect match to the case company's investigated review question (data testing). However, we extracted relevant approaches from the five studies on a conceptual level to support later context-specific improvements. We found the interactive rapid review approach useful for triggering and aligning communication between the different stakeholders.