A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine Learning

In software engineering community, defect prediction is one the active domain. For the software's success, it is essential to reduce the software engineering and data-mining gap. Software defects prediction forecasts the source code errors before the testing phase. Methods for predicting software defects, such as clustering, statistical methods, mixed algorithms, metrics based on neural networks, black box testing, white box testing and machine learning are frequently used to explore the effect area in software. The main contribution of this research is the use of feature selection for the first time to increase the accuracy of machine learning classifiers in defects pre-diction. The objective of this study is to improve the defects prediction accuracy in five data sets of NASA namely; CM1, JM1, KC2, KC1, and PC1. These NASA data sets are open to public. In this research, the feature selection technique is use with machine-learning techniques; Random Forest, Logistic Regression, Multilayer Perceptron, Bayesian Net, Rule ZeroR, J48, Lazy IBK, Support Vector Machine, Neural Networks, and Decision Stump to achieve high defect prediction accuracy as compared to without feature selection (WOFS). The research workbench, a machine-learning tool called WEKA (Waikato Environment for Knowledge Analysis), is used to refine da-ta, preprocess data, and apply the mentioned classifiers. To assess statistical analyses, a mini tab statistical tool is used. The results of this study reveals that accuracy of defects prediction with feature selection (WFS) is improve in contrast with the accuracy of WOFS.