Software Defect Prediction Via Deep Learning

Abstract

Existing models on defect prediction are trained on historical limited data which has been studied from a variety of pioneering and researchers. Cross-project defect prediction, which is often reuse data from other projects, works well when the data of training models is completely sufficient to meet the project demands. However, current studies on software defect prediction require some degree of heterogeneity of metric values that does not always lead to accurate predictions. Inspired by the current research studies, this paper takes the benefit with the state-of-the-art of deep learning and random forest to perform various experiments using five different datasets. Our model is ideal for predicting of defects with 90% accuracy using 10-fold cross-validation. The achieved results show that Random Forest and Deep learning are giving more accurate predictions with compared to Bayes network and SVM on all five datasets. We also derived Deep Learning that can be competitive classifiers and provide more robust for detecting defect prediction.