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TAF: a tool for diverse and constrained test case generation

Abstract : The generation of test cases may have to accommodate size-varying data structures and semantic constraints between the data elements. This often requires the development of custom generators. In this paper, we introduce a novel generic tool to generate constrained and diverse test cases from a data model. First, the user defines the model using an XML-based domain-specific language. Then TAF generates diverse test cases by combining random sampling with the use of an SMT solver. The capabilities of the tool are demonstrated by four examples of models coming from various application domains: virtual crop fields for testing an agriculture robot, bitmap images with a graduated background, a population of taxpayers in a tax management system, and tree structures of diverse sizes and heights. We show how TAF performs in terms of data diversity and execution time. We also provide some comparison results with an UML-based tool using SMT solving.
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Contributor : Jérémie Guiochet Connect in order to contact the contributor
Submitted on : Friday, November 19, 2021 - 9:19:48 AM
Last modification on : Monday, July 4, 2022 - 8:43:17 AM
Long-term archiving on: : Sunday, February 20, 2022 - 6:22:05 PM


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Clément Robert, Jérémie Guiochet, Hélène Waeselynck, Luca Vittorio Sartori. TAF: a tool for diverse and constrained test case generation. 21st IEEE International Conference on Software Quality, Reliability and Security (QRS), Dec 2021, Hanan Island, China. ⟨10.1109/QRS54544.2021.00042⟩. ⟨hal-03435959⟩



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