Detection of web service anti-patterns using machine learning framework

Abstract

Web services are being embraced by IT industry in the recent past to enable rapid development of distributed systems with optimal cost. Web services in SOA are self-adaptable to context, which makes SOA widely recognized in IT system as the technology, which has the potential of improving the receptiveness of both business and IT organizations. Web services help in building Service Based Systems (SBS) like Paytm, Amazon, Paypal, e-bay etc. which evolves frequently to fit the new user requirements which impacts the evolvability and quality of software design. Similar to software systems built using other paradigms, Service based systems also suffer from bad or poor design choices as in anti-pattern, code smells etc. Anti-patterns are explicit structures in the design that indicates violation of fundamental design principles and negatively impact the design quality. Anti-patterns have obstructive influence on the maintainability and perception of software systems. Thus there is a rising need for the early prediction of anti-patterns and refactoring them to improve the software quality in terms of execution cost, maintenance cost and memory consumption. In this work, a frame work is proposed for significant feature selection from source code metrics which includes Wilcoxon signed rank test, Univariate logistic regression analysis and Cross-correlation analysis. Then the different sets of features from various steps along with the original source code metrics are considered and are used for anti-pattern detection using 13 machine learning algorithms. Experimental results show the approximation capability of different classifiers and data balancing techniques with the features selected from the various steps of feature validation framework in addition to the original features for developing anti-pattern prediction model. The results also shows that the prediction model built with by the ensemble