Author:
J. I. Olszewska
Affiliation:
School of Computing and Engineering, University of West Scotland, U.K.
Keyword(s):
Intelligent Systems, Software Testing, Software Engineering Ontology, Ontological Domain Analysis and Modeling, Knowledge Engineering, Knowledge Representation, Interoperability, Decision Support Systems, Transparency, Accountability, Unbiased Machine Learning, Explainable Artificial Intelligence (XAI).
Abstract:
Software testing is an expanding area which presents an increasing complexity. Indeed, on one hand, there is the development of technologies such as Software Testing as a Service (TaaS), and on the other hand, there is a growing number of Artificial Intelligence (AI)-based softwares. Hence, this work is about the development of an ontological framework for AI-softwares’ Testing (AI-T), which domain covers both software testing and explainable artificial intelligence; the goal being to produce an ontology which guides the testing of AI softwares, in an effective and interoperable way. For this purpose, AI-T ontology includes temporal interval logic modelling of the software testing process as well as ethical principle formalization and has been built using the Enterprise Ontology (EO) methodology. Our resulting AI-T ontology proposes both conceptual and implementation models and contains 708 terms and 706 axioms.