Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts

• URL: http://arxiv.org/abs/2108.13837v1
• Date: Tue, 31 Aug 2021 13:50:15 GMT
• Title: Towards a Common Testing Terminology for Software Engineering and Artificial Intelligence Experts
• Authors: Lisa J\"ockel, Thomas Bauer, Michael Kl\"as, Marc P. Hauer, Janek Gro{\ss}
• Abstract summary: This paper contributes a mapping between the most important concepts from classical software testing and AI testing. In the mapping, we highlight differences in relevance and naming of the mapped concepts.
• Score: 0.9799637101641152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 development of new concepts for AI would benefit from a deeper understanding and exchange between AI and software engineering experts. A major obstacle on this way, we see in the different terminologies used in the two communities. As we consider a mutual understanding of the testing terminology as 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 relevance and naming of the mapped concepts.

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