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Area of Science:

  • Software Engineering
  • Web Services
  • Quality of Service (QoS)

Background:

  • The proliferation of similar Web services complicates discovery and selection.
  • Quality of Service (QoS) is essential for choosing appropriate services.
  • Service registries lack guaranteed validity of reported QoS values.

Purpose of the Study:

  • To propose a novel methodology for predicting Web service QoS.
  • To leverage source code metrics for QoS prediction.
  • To enhance the efficiency of Web service discovery and selection.

Main Methods:

  • Aggregating software metrics using inequality distribution from class to entire Web service level.
  • Training a machine learning model using the correlation between QoS and software metrics.
  • Validating and evaluating the approach with three sets of software quality metrics.

Main Results:

  • The proposed methodology effectively predicts Web service QoS properties.
  • Source code metrics demonstrate a strong correlation with QoS.
  • The approach improves the efficiency of QoS prediction.

Conclusions:

  • Predicting Web service QoS using source code metrics is feasible and effective.
  • This method offers a reliable alternative to potentially invalid service registry data.
  • The findings contribute to more efficient and accurate service-oriented system development.