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Analytical quality assurance, especially testing, is an integral part ofsoftware-intensive system development. With the increased usage of ArtificialIntelligence (AI) and Machine Learning (ML) as part of such systems, thisbecomes more difficult as well-understood software testing approaches cannot beapplied directly to the AI-enabled parts of the system. The required adaptationof classical testing approaches and the development of new concepts for AIwould benefit from a deeper understanding and exchange between AI and softwareengineering experts. We see the different terminologies used in the twocommunities as a major obstacle on this way. As we consider a mutualunderstanding of the testing terminology a key, this paper contributes amapping between the most important concepts from classical software testing andAI testing. In the mapping, we highlight differences in the relevance andnaming of the mapped concepts.

Towards a Common Testing Terminology for Software Engineering and Data Science Experts