Detection of Web Service Anti-patterns Using Neural Networks with Multiple Layers

Abstract

Anti-patterns in service-oriented architecture are solutions to common issues where the solution is ineffective and may end up in undesired consequences. It is a standard exercise that initially seems like the best solution; however, it finally ends up having bad results that outweigh any benefits. Research revealed that the presence of anti-patterns leads to the demeaning of the quality and design of the software systems, which makes the process of detecting anti-patterns in web services very crucial. In this work, we empirically investigate the effectiveness of three feature sampling techniques, five data sampling techniques, and six classification algorithms in the detection of web service anti-patterns. Experiment results revealed that the model developed by considering metrics selected by Principal Component Analysis (PCA) as the input obtained better performance compared to the model developed by other metrics. Experimental results also showed that the neural network model developed with two hidden layers has outperformed all the other models developed with varying number of hidden layers.