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    The impact of feature selection on maintainability prediction of service-oriented applications

    Access Status
    Fulltext not available
    Authors
    Kumar, L.
    Krishna, Aneesh
    Rath, S.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Kumar, L. and Krishna, A. and Rath, S. 2016. The impact of feature selection on maintainability prediction of service-oriented applications. Service Oriented Computing and Applications. 11 (2): pp. 137-161.
    Source Title
    Service Oriented Computing and Applications
    DOI
    10.1007/s11761-016-0202-9
    ISSN
    1863-2386
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/53802
    Collection
    • Curtin Research Publications
    Abstract

    Service-oriented development methodologies are very often considered for distributed system development. The quality of service-oriented computing can be best assessed by the use of software metrics that are considered to design the prediction model. Feature selection technique is a process of selecting a subset of features that may lead to build improved prediction models. Feature selection techniques can be broadly classified into two subclasses such as feature ranking and feature subset selection technique. In this study, eight different types of feature ranking and four different types of feature subset selection techniques have been considered for improving the performance of a prediction model focusing on maintainability criterion. The performance of these feature selection techniques is evaluated using support vector machine with different types of kernels over a case study, i.e., five different versions of eBay Web service. The performances are measured using accuracy and F-measure value. The results show that maintainability of the service-oriented computing paradigm can be predicted by using object-oriented metrics. The results also show that it is possible to find a small subset of object-oriented metrics which helps to predict maintainability with higher accuracy and also reduces the value of misclassification errors.

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