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.

Highlights

  • Service-Oriented Architecture (SOA) has become a platform for processing large amounts of information and knowledge to provide essential data and services to the users [1, 2]

  • We investigate the impact of source code metrics aggregation on correlation between Quality of Service (QoS) attributes and source code metrics

  • The popular aggregation technique used for source code metrics is mean [32], [33] even though there are increasing research works to demonstrate the inappropriateness of this technique [34], [35] due to the skewness of source code metrics distribution [36]

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Summary

Introduction

Service-Oriented Architecture (SOA) has become a platform for processing large amounts of information and knowledge to provide essential data and services to the users [1, 2]. Web service QoS prediction using improved software source code metrics functionality keyword is no longer valid. If a user want to test Web service by themselves end up paying considerable amount of money It is not practically possible for a user to monitor and collect QoS data for all the functionally similar WSs. 4. Source code metrics are calculated at micro-level and aggregated into macro level to represent the entire software efficiently. Impact of aggregation schemes for source code metrics on Web service QoS prediction remains unexplored. Web service QoS prediction using improved software source code metrics prediction models may be negatively affected by the potential loss of information due to summation and aggregation.

Problem specification
Software source code metrics
Correlation between source code metrics and QoS
Software metrics aggregation
Source code metrics aggregation
Learning machine
Variables and objects
Empirical environment
Result analysis
Findings
Conclusion
Full Text
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