AI-T: software testing ontology for AI-based systems

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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.
Original languageEnglish
Title of host publicationProceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Number of pages8
ISBN (Print)9789897584749
Publication statusPublished - 2020
Event12th International Conference on Knowledge Engineering and Ontology Development - Budapest, Hungary
Duration: 2 Nov 20204 Nov 2020


Conference12th International Conference on Knowledge Engineering and Ontology Development
Abbreviated titleKEOD 2020
Internet address


  • 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)


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