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There is a growing interest in industry and academia in machine learning (ML)testing. We believe that industry and academia need to learn together toproduce rigorous and relevant knowledge. In this study, we initiate acollaboration between stakeholders from one case company, one researchinstitute, and one university. To establish a common view of the problemdomain, we applied an interactive rapid review of the state of the art. Fourresearchers from Lund University and RISE Research Institutes and fourpractitioners from Axis Communications reviewed a set of 180 primary studies onML testing. We developed a taxonomy for the communication around ML testingchallenges and results and identified a list of 12 review questions relevantfor Axis Communications. The three most important questions (data testing,metrics for assessment, and test generation) were mapped to the literature, andan in-depth analysis of the 35 primary studies matching the most importantquestion (data testing) was made. A final set of the five best matches wereanalysed and we reflect on the criteria for applicability and relevance for theindustry. The taxonomies are helpful for communication but not final.Furthermore, there was no perfect match to the case company's investigatedreview question (data testing). However, we extracted relevant approaches fromthe five studies on a conceptual level to support later context-specificimprovements. We found the interactive rapid review approach useful fortriggering and aligning communication between the different stakeholders.

Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications