  Analytical quality assurance, especially testing, is an integral part of
software-intensive system development. With the increased usage of Artificial
Intelligence (AI) and Machine Learning (ML) as part of such systems, this
becomes more difficult as well-understood software testing approaches cannot be
applied directly to the AI-enabled parts of the system. The required adaptation
of classical testing approaches and the development of new concepts for AI
would benefit from a deeper understanding and exchange between AI and software
engineering experts. We see the different terminologies used in the two
communities as a major obstacle on this way. As we consider a mutual
understanding of the testing terminology a key, this paper contributes a
mapping between the most important concepts from classical software testing and
AI testing. In the mapping, we highlight differences in the relevance and
naming of the mapped concepts.
