TAF: a tool for diverse and constrained test case generation

Clément Robert (1) , Jérémie Guiochet (1) , Hélène Waeselynck (1) , Luca Vittorio Sartori (1)

1 LAAS-TSF - Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique

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.

Mots clés: test; test input generation; fuzzing; autonomous robots; simulation

Domaines: Informatique [cs]

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⟩