Software Defect Prediction Via Deep Learning
Rehan Ullah Khan1, Saleh Albahli2, Waleed Albattah3, Mohammad Nazrul Islam Khan4
1Rehan Ullah Khan, Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
2Saleh Albahli, Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
3Waleed Albattah, Department of Information Technology, College of Computer, Qassim University, Saudi Arabia.
4Mohammad Nazrul Islam Khan, Department of Computer Engineering, College of Computer, Qassim University, Saudi Arabia.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 10, 2020. | PP: 343-349 | Volume-9 Issue-5, March 2020. | Retrieval Number: D1858029420/2020©BEIESP | DOI: 10.35940/ijitee.D1858.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Defect Prediction; Deep Learning; Software repository mining; Cross-Project; Class imbalance.
Scope of the Article: Deep Learning