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

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Abstract

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.
Original languageEnglish
Title of host publication2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-4503-9275-4
ISBN (Print)978-1-6654-5206-9
Publication statusPublished - 2022 May 16
Event2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) - Pittsburg, United States
Duration: 2022 May 162022 May 17

Conference

Conference2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN)
Country/TerritoryUnited States
CityPittsburg
Period2022/05/162022/05/17

Subject classification (UKÄ)

  • Software Engineering

Free keywords

  • AI Engineering
  • Machine Learning Testing
  • Interactive Rapid Review
  • Taxonomy

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