Ontology-based Software Test Case Generation (OSTAG)Show others and affiliations
2015 (English)In: European Projects in Knowledge Applications and Intelligent Systems - Volume 1: EPS Lisbon 2016 / [ed] R. J. Machado, J. Sequeira,H. Plácido da Silva, & J. Filipe, SciTePress, 2015, p. 135-159Conference paper, Published paper (Refereed)
Abstract [en]
Testing is a paramount quality assurance activity in every software developmentproject, especially for embedded, safety critical systems. During thetest process, a lot of effort is put into the generation of test cases. The presentedOSTAG project aimed at developing methods and techniques to automate thesoftware test case generation for black-box testing. The proposed approach wasbased on the creation of a software requirements ontology and the applicationof inference rules on the ontology to derive test cases. The ontology representsknowledge of the requirements, the software system and the corresponding applicationdomain while the inference rules formalize knowledge from documentsand experienced testers in the domain of test planning and test case generation.A software prototype of the approach was implemented and one of the industrialproject partners evaluated the results. An alternative method for generating testcases, based on genetic algorithms, was also explored.
Place, publisher, year, edition, pages
SciTePress, 2015. p. 135-159
Keywords [en]
Black-box Testing, Embedded Systems, Genetic Algorithms, Inference Rules, Knowledge Modelling, Model-Based Testing, Ontology Development, Ontology Quality Evaluation, Ontology Verbalisation, OWL, Prolog, Prot´eg´e, Software Requirements Specification, Test Case Generation.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hj:diva-43426DOI: 10.5220/0007901301350159ISBN: 978-989-758-356-8 (print)OAI: oai:DiVA.org:hj-43426DiVA, id: diva2:1302404
Conference
European Projects in Knowledge Applications and Intelligent Systems, July 20-23, 2016, Lisbon, Portugal
Funder
Knowledge Foundation, 201401702019-04-042019-04-042025-10-13Bibliographically approved