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.

本研究旨在探究机器学习测试的相关问题，并通过快速审核等方法建立了与业界、学术界等相关方的共同视角，分析了一系列文献，建立了相关分类学，并提出了 12 个问题用于检验与测试机器学习的质量及可行性，针对数据测试等方面进行了深入研究，并从五个研究中提取相关方法以支持日后的改进工作。