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    Item type:Publication, Access status: without files ,
    Data-Driven Test Generation for Black-Box Systems From Learned Decision Tree Models
    (VDE Verlag, 2023-03)
    Plambeck, Swantje
    ;
    Fey, Görschwin
    Testing black-box systems is a difficult task, because no prior knowledge on the system is given that can be used for design and evaluation of tests. Learning a model of a black-box system from observations enables Model-Based Testing (MBT). We take a recent approach using decision tree learning to create a model of a black-box system and discuss the usage of such a decision tree model for test generation. A decision tree model especially facilitates MBT for black-box systems if no system reset is possible. A case study on a discrete system illustrates our MBT approach.
    Publicationtype: Conference Paper
      20
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    Item type:Publication, Access status: without files ,
    Extended Abstract: Data-Driven Test Generation for Black-Box Systems From Learned Decision Tree Models
    (2023-03)
    Plambeck, Swantje
    ;
    Fey, Görschwin
    Testing black-box systems is a difficult task, because no prior knowledge on the system is given that can be used for design and evaluation of tests. Learning a model of a black-box system from observations enables Model-Based Testing (MBT). We take a recent approach using decision tree learning to create a model of a black-box system and discuss the usage of such a decision tree model for test generation. A decision tree model especially facilitates MBT for black-box systems if no system reset is possible. A case study on a discrete system illustrates our MBT approach.
    Publicationtype: Conference Paper
      9