Towards a Common Testing Terminology for Software Engineering and Data Science Experts
This work addresses the problem of terminology barriers for researchers and practitioners integrating AI into software systems, but it is incremental as it focuses on mapping existing concepts rather than introducing new methods.
The paper tackles the challenge of applying software testing approaches to AI-enabled systems by proposing a mapping between classical software testing and AI testing terminologies to facilitate understanding between software engineering and data science experts.
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.