An Empirical Analysis on Web Service Anti-pattern Detection Using a Machine Learning Framework

Web Services are application components characterised by interoperability, extensibility, distributed application development and service oriented architecture. A complex distributed application can be developed by combing several third-party web-services. Anti-patterns are counter-productive and poor design and practices. Web-services suffer from a multitude of anti-patterns such as God object Web service and Fine grained Web service. Our work is motivated by the need to build techniques for automatically detecting common web-services anti-patterns by static analysis of the source code implementing a web-service. Our approach is based on the premise that summary values of object oriented source code metrics computed at a web-service level can be used as a predictor for anti-patterns. We present an empirical analysis of 4 data sampling techniques to encounter the class imbalance problem, 5 feature ranking techniques to identify the most informative and relevant features and 8 machine learning algorithms for predicting 5 different types of anti-patterns on 226 real-world web-services across several domains. We conclude that it is possible to predict anti-patterns using source code metrics and a machine learning framework. Our analysis reveals that the best performing classification algorithm is Random Forest, best performing data sampling technique is SMOTE and the best performing feature ranking method is OneR.