Improved Random Forest Algorithm for Software Defect Prediction through Data Mining Techniques

Software defect prediction using classification algorithms was advocated by many researchers.Moreover the classifier ensemble can effectively improve classification performance compared to a single classifier. The research on defect prediction using classifier ensemble methods are motivated since they have not been fully exploited.Software defects leads to failure of many defense systems. A comparative study of various classification methods was performed to classify software defects. The methods include Random Tree, Random Forest, Bayesian Network, Naive Bayes, K-Nearest Neighbour and Instance Based Classifier.Random Forest algorithm was found to give more accurate prediction than other classifiers.