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Web service QoS prediction using improved software source code metrics

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  • Sarathkumar Rangarajan
  • Huai Liu
  • Hua Wang

Abstract

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services’ QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics.

Suggested Citation

  • Sarathkumar Rangarajan & Huai Liu & Hua Wang, 2020. "Web service QoS prediction using improved software source code metrics," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-25, January.
    • Handle: RePEc:plo:pone00:0226867
    DOI: 10.1371/journal.pone.0226867
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    References listed on IDEAS

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    1. Lov Kumar & Mukesh Kumar & Santanu Ku. Rath, 2017. "Maintainability prediction of web service using support vector machine with various kernel methods," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 205-222, June.
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