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Conference Papers Year : 2016

Risk-Based Interoperability Testing Using Reinforcement Learning

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André Reichstaller
  • Function : Author
  • PersonId : 1023402
Benedikt Eberhardinger
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  • PersonId : 1023363
Alexander Knapp
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  • PersonId : 1023403
Wolfgang Reif
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  • PersonId : 1023366
Marcel Gehlen
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  • PersonId : 1023404

Abstract

Risk-based test strategies enable the tester to harmonize the number of specified test cases with imposed time and cost constraints. However, the risk assessment itself often requires a considerable effort of cost and time, since it is rarely automated. Especially for complex tasks such as testing the interoperability of different components it is expensive to manually assess the criticality of possible faults. We present a method that operationalizes the risk assessment for interoperability testing. This method uses behavior models of the system under test and reinforcement learning techniques to break down the criticality of given failure situations to the relevance of single system actions for being tested. Based on this risk assessment, a desired number of test cases is generated which covers as much relevance as possible. Risk models and test cases have been generated for a mobile payment system within an industrial case study.
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Dates and versions

hal-01643732 , version 1 (21-11-2017)

Licence

Attribution - CC BY 4.0

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André Reichstaller, Benedikt Eberhardinger, Alexander Knapp, Wolfgang Reif, Marcel Gehlen. Risk-Based Interoperability Testing Using Reinforcement Learning. 28th IFIP International Conference on Testing Software and Systems (ICTSS), Oct 2016, Graz, Austria. pp.52-69, ⟨10.1007/978-3-319-47443-4_4⟩. ⟨hal-01643732⟩
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