TAF: a Tool for Diverse and Constrained Test Case Generation | IEEE Conference Publication | IEEE Xplore

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 ...Show More

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.
Date of Conference: 06-10 December 2021
Date Added to IEEE Xplore: 10 March 2022
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Conference Location: Hainan, China

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