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

TL;DR

This paper proposes a unified testing terminology to bridge the gap between software engineering and AI experts, facilitating better understanding and adaptation of testing approaches for AI-enabled systems.

Contribution

It provides a mapping between classical software testing concepts and AI testing terminology, highlighting differences and promoting shared understanding.

Findings

Identified key differences in testing terminology between AI and software engineering.

Developed a mapping to align concepts across both domains.

Facilitates improved communication and testing practices for AI systems.

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 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.