Using source code metrics to predict change-prone web services: A case-study on ebay services

Abstract

Predicting change-prone object-oriented software using source code metrics is an area that has attracted several researchers attention. However, predicting change-prone web services in terms of changes in the WSDL (Web Service Description Language) Interface using source code metrics implementing the services is a relatively unexplored area. We conduct a case-study on change proneness prediction on an experimental dataset consisting of several versions of eBay web services wherein we compute the churn between different versions of the WSDL interfaces using the WSDLDiff Tool. We compute 21 source code metrics using Chidamber and Kemerer Java Metrics (CKJM) extended tool serving as predictors and apply Least Squares Support Vector Machines (LSSVM) based technique to develop a change proneness estimator. Our experimental results demonstrates that a predictive model developed using all 21 metrics and linear kernel yields the best results.