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Filtrado por: Fecha de creacion 2019-09-02 Eliminar la restricciónFecha de creacion: 2019-09-02 Departamento - Computing Sciences & Informatics Eliminar la restricciónDepartamento: - Computing Sciences & Informatics

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  • A Process Mining Approach to Improving Defect Detection of SysML Models

    Article thumbnail: A Process Mining Approach to Improving Defect Detection of SysML Models
    Type:
    Article
    Descripción/Resumen:
    The development of complex and dependable systems like autonomous vehicles relies increasingly on the use of systems modeling language (SysML). In fact, SysML has become a de facto standard for systems engineering. With model-driven engineering, a SysML model serves as a reference for the early defect detection of the system under design: the earlier the errors are detected, the less is the cost of handling the errors. Mutation testing is a fault-based technique that has recently seen its applications to SysML behavioral models (e.g., state machine diagrams). Specifically, a system's state-transition design can be fed to a model checker where mutants are automatically generated and then killed against the desired design specifications (e.g., safety properties). In this paper, we present a novel approach based on process mining to improve the effectiveness and efficiency of the SysML mutation testing based on model checking. In our approach, the mutation operators are applied directly to the state machine diagram. These mutants are then fed as traces into a process mining tool and checked according to the event logs. Our initial results indicates that the process mining approach kills more mutants faster than the model checking method.
    Creador/Autor:
    Alenazi, Mounifah; Niu, Nan, and Savolainen, Juha
    Peticionario:
    Nan Niu
    Fecha modificada:
    09/02/2019
    Fecha modificada:
    09/05/2019
    Fecha de creacion:
    2019-09-02
    Licencia:
    Open Data Commons Attribution License (ODC-By)