Improving web services design quality using dimensionality reduction techniques

Abstract

In this paper, we propose a dimensionality reduction approach based on PCA-NSGAII to address the Web services modularization problem. Our approach aims at finding the best reduced set of objectives (e.g. quality metrics) that can generate near optimal modularization solutions to fix quality issues in Web services interface. The algorithm starts with a large number of Web service quality metrics as objectives that are reduced based on the correlation between them. This correlation is identified during the execution of the multi-objective algorithm by mining the execution traces of the generated solutions and their evaluations. We evaluated our approach on a set of 22 real world Web services, provided by Amazon and Yahoo. Statistical analysis of our experiments shows that our dimensionality reduction Web services interface modularization approach performed significantly better than the state-of-the-art modularization techniques in terms of generating well-designed Web services interface for users.