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dc.contributor.authorŞahin Gebizli, C.
dc.contributor.authorMetin, D.
dc.contributor.authorSözer, Hasan
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractModel-based testing employs models of the system under test to automatically generate test cases. In this paper, we propose an iterative approach, in which these models are refined based on the principles of risk-based testing. We use Markov Chains as system models, in which transitions among system states are annotated with probabilities. Initially, these probability values are equal and as such, states have equal chances for being visited by the generated test cases. Memory leaks are monitored during the execution of these test cases. Then, transition probabilities are updated based on the risk that a failure can occur due to the observed memory leaks. We applied our approach in the context of an industrial case study for model-based testing of a Smart TV system. We observed promising results, in which several crash failures were detected after an iteration of model refinement. We aim at automating the whole process based on an adaptation model using the history of recorded memory leaks during previous test executions.
dc.relation.ispartofSoftware Testing, Verification and Validation Workshops (ICSTW), 2015 IEEE Eighth International Conference on
dc.titleCombining model-based and risk-based testing for effective test case generationen_US
dc.typeConference paperen_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-2968-4763 & YÖK ID 23178) Sözer, Hasan
dc.contributor.ozuauthorSözer, Hasan
dc.subject.keywordsMemory stress testing
dc.subject.keywordsModel-based testing
dc.subject.keywordsRiskbased testing
dc.subject.keywordsIndustrial case study
dc.subject.keywordsTest automation
dc.subject.keywordsSmart TV
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff

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