An Empirical Study to Investigate Different SMOTE Data Sampling Techniques for Improving Software Refactoring Prediction

The exponential rise in software systems and allied applications has alarmed industries and professionals to ensure high quality with optimal reliability, maintainability etc. On contrary software companies focus on developing software solutions at the reduced cost corresponding to the customer demands. Thus, maintaining optimal software quality at reduced cost has always been the challenge for developers. On the other hand, inappropriate code design often leads aging, smells or bugs which can harm eventual intend of the software systems. However, identifying a smell signifier or structural attribute characterizing refactoring probability in software has been the challenge. To alleviate such problems, in this research code-metrics structural feature identification and Neural Network based refactoring prediction model is developed. Our proposed refactoring prediction system at first extracts a set of software code metrics from object-oriented software systems, which are then processed for feature selection method to choose an appropriate sample set of features using Wilcoxon rank test. Once obtaining the optimal set of code-metrics, a novel ANN classifier using 5 different hidden layers is implemented on 5 open source java projects with 3 data sampling techniques SMOTE, BLSMOTE, SVSMOTE to handle class imbalance problem. The performance of our proposed model achieves optimal classification accuracy, F-measure and then it has been shown through AUC graph as well as box-plot diagram.