Ontology-based Testing: An Emerging Paradigm for Modeling and Testing Systems and Software

Franz Wotawa, Josip Bozic, Yihao Li

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

Model-based testing has been successfully applied for test case generation in practice. Its underlying idea is to utilize models of the system for obtaining system inputs and their corresponding expected outputs. In this paper, we report on experiences gained when using a different methodology relying on models, i.e., ontology-based testing, for generating test suites in practice. Instead of modeling the system’s behavior, ontology-based testing relies on models of the system’s environment, i.e., an environmental ontology. Test cases are generated from ontologies converting them into an input model for combinatorial testing, and using a combinatorial testing algorithm for finally computing the test cases. We show how ontology-based testing can be applied in three different application domains, i.e., testing autonomous driving functionality, security testing, and compiler testing, discuss issues arising and indicate future research objectives.
Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 13th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2020
PublisherIEEE Publications
Pages14-17
Number of pages4
ISBN (Electronic)978-1-7281-1075-2
DOIs
Publication statusPublished - 2020
Event2020 IEEE International Conference on Software Testing, Verification and Validation : ICSTW 2020 - Hybrider Event, Portugal
Duration: 24 Oct 202028 Oct 2020
https://icst2020.info/

Conference

Conference2020 IEEE International Conference on Software Testing, Verification and Validation
Abbreviated titleICSTW 2020
CountryPortugal
CityHybrider Event
Period24/10/2028/10/20
Internet address

Keywords

  • compiler testing
  • model-based testing
  • Ontologies
  • security testing
  • testing autonomous systems

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality
  • Modelling and Simulation

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