<div class="csl-bib-body">
<div class="csl-entry">Paradzikovic, P., Hoch, R., & Kaindl, H. (2022). Assigning Systems to Test Environments Through Ontological Reasoning. In <i>Towards a Knowledge-Aware AI</i> (pp. 75–89). IOS Press. https://doi.org/10.3233/SSW220011</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/135919
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dc.description.abstract
In the automotive industry, testing for reliability and safety is very important but costly. Due to the deployment of an increasing number of features within these systems, mapping them to compatible test environments becomes more and more complex. In this paper, we present a use case for applying ontological reasoning in the automotive industry for supporting testers while making the selection of test environments. The given task has been to map the software under test together with test cases to test environments through ontological reasoning. To this end, we defined an ontology of test environments. It can be used for ontological reasoning, both by applying instance classification and subsumption reasoning, to assign test environments. This approach is prototypically implemented in Stardog, in combination with OWL2 and SPARQL. It is deployed alongside existing software at our industry partner’s premises and provides a user interface, which supports testers while selecting test environments and executing tests.
en
dc.language.iso
en
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dc.relation.ispartofseries
Studies on the Semantic Web
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Ontology
en
dc.subject
subsumption
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dc.subject
test environments
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dc.subject
automotive software
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dc.title
Assigning Systems to Test Environments Through Ontological Reasoning