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Filtering by: Creator/Author Alenazi, Mounifah Remove constraint Creator/Author: Alenazi, Mounifah Publisher University of Cincinnati Remove constraint Publisher: University of Cincinnati

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  • Data of SysML SMD Environment Assumptions

    Generic Work thumbnail: Data of SysML SMD Environment Assumptions
    Type:
    Generic Work
    Description/Abstract:
    Environment assumptions elicited from human modelers for a SysML state machine diagram (SMD) in the context of satisfaction of a safety requirement.
    Creator/Author:
    Alenazi, Mounifah and Niu, Nan
    Submitter:
    Nan Niu
    Date Uploaded:
    05/22/2020
    Date Modified:
    05/22/2020
    Date Created:
    2020-05-22
    License:
    Open Data Commons Open Database License (ODbL)

  • 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
    Description/Abstract:
    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.
    Creator/Author:
    Alenazi, Mounifah; Niu, Nan, and Savolainen, Juha
    Submitter:
    Nan Niu
    Date Uploaded:
    09/02/2019
    Date Modified:
    09/05/2019
    Date Created:
    2019-09-02
    License:
    Open Data Commons Attribution License (ODC-By)

  • Data of SysML Modeling Mistakes

    Generic Work thumbnail: Data of SysML Modeling Mistakes
    Type:
    Generic Work
    Description/Abstract:
    Data extraction results of a systematic mapping on the SysML modeling mistakes
    Creator/Author:
    Alenazi, Mounifah; Niu, Nan, and Savolainen, Juha
    Submitter:
    Nan Niu
    Date Uploaded:
    06/16/2019
    Date Modified:
    06/30/2019
    Date Created:
    2019-06-15
    License:
    Open Data Commons Open Database License (ODbL)

  • Subject Systems Dataset

    Dataset thumbnail: Subject Systems Dataset
    Type:
    Dataset
    Description/Abstract:
    Anti-Lock Braking Systems Obstacles. RPM obstacles.
    Creator/Author:
    Alenazi, Mounifah; Niu, Nan; Wang, Wentao, and Savolainen Juha
    Submitter:
    Mounifah Alenazi
    Date Uploaded:
    05/31/2018
    Date Modified:
    05/23/2019
    Date Created:
    2018-06
    License:
    All rights reserved