Web-services classification using intelligent techniques

The web services, a novel paradigm in software technology, have innovative mechanism for rendering services over diversified environment. They promise to allow businesses to adapt rapidly to changes in the business environment and the needs of different customers. The rapid introduction of new web services into a dynamic business environment can adversely affect the service quality and user satisfaction. Consequently, assessment of the quality of web services is of paramount importance in selecting a web service for an application. In this paper, we employed well-known classification models viz., back propagation neural network (BPNN), probabilistic neural network (PNN), group method of data handling (GMDH), classification and regression trees (CART), TreeNet, support vector machine (SVM) and ID3 decision tree (J48) to predict the quality of a web service based on a set of quality attributes. The experiments are carried out on the QWS dataset. We applied 10-fold cross-validation to test the efficacy of the models. The J48 and TreeNet techniques outperformed all other techniques by yielding an average accuracy of 99.72%. We also performed feature selection and found that web-services relevance function (WSRF) is the most significant attribute in determining the quality of a web service. Later, we performed feature selection without WSRF and found that Reliability, Throughput, Successability, Documentation and ResponseTime are the most important attributes in that order. Moreover, the set of 'if-then' rules yielded by J48 and CART can be used as an expert system for web-services classification.