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

Joanna Isabelle Olszewska

doi:10.5220/0010147902910298

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

Joanna Isabelle Olszewska

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

138 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Publisher	SciTePress
Pages	291-298
Number of pages	8
Volume	2
ISBN (Print)	9789897584749
DOIs	https://doi.org/10.5220/0010147902910298
Publication status	Published - 2020
Event	12th International Conference on Knowledge Engineering and Ontology Development - Budapest, Hungary Duration: 2 Nov 2020 → 4 Nov 2020 http://www.keod.ic3k.org/

Conference

Conference	12th International Conference on Knowledge Engineering and Ontology Development
Abbreviated title	KEOD 2020
Country/Territory	Hungary
City	Budapest
Period	2/11/20 → 4/11/20
Internet address	http://www.keod.ic3k.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production
SDG 16 Peace, Justice and Strong Institutions

Keywords

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)

Access to Document

10.5220/0010147902910298Licence: CC BY-NC-ND

2020 09 14 Olszewska AI-T acceptedAccepted author manuscript, 217 KBLicence: CC BY-NC-ND

Testing Intelligent Vision Systems
Olszewska, J.
25/06/21
1 item of Media coverage
Press/Media: Expert Comment

Cite this

@inproceedings{62644f5db6d64efcb8fff51568df5fa9,

title = "AI-T: software testing ontology for AI-based systems",

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{\textquoteright} 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.",

keywords = "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)",

author = "Olszewska, \{Joanna Isabelle\}",

year = "2020",

doi = "10.5220/0010147902910298",

language = "English",

isbn = "9789897584749",

volume = "2",

pages = "291--298",

booktitle = "Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",

publisher = "SciTePress",

address = "Portugal",

note = "12th International Conference on Knowledge Engineering and Ontology Development, KEOD 2020 ; Conference date: 02-11-2020 Through 04-11-2020",

url = "http://www.keod.ic3k.org/",

}

Olszewska, JI 2020, AI-T: software testing ontology for AI-based systems. in Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. vol. 2, SciTePress, pp. 291-298, 12th International Conference on Knowledge Engineering and Ontology Development, Budapest, Hungary, 2/11/20. https://doi.org/10.5220/0010147902910298

TY - GEN

T1 - AI-T

T2 - 12th International Conference on Knowledge Engineering and Ontology Development

AU - Olszewska, Joanna Isabelle

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - intelligent systems

KW - software testing

KW - software engineering ontology

KW - ontological domain analysis and modeling

KW - knowledge engineering

KW - knowledge representation

KW - interoperability

KW - decision support systems

KW - transparency

KW - accountability

KW - unbiased machine learning

KW - explainable artificial intelligence (XAI)

U2 - 10.5220/0010147902910298

DO - 10.5220/0010147902910298

M3 - Conference contribution

SN - 9789897584749

VL - 2

SP - 291

EP - 298

BT - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

PB - SciTePress

Y2 - 2 November 2020 through 4 November 2020

ER -

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

Abstract

Conference

UN SDGs

Keywords

Access to Document

Fingerprint

Press/Media

Testing Intelligent Vision Systems

Cite this