Software Defect Prediction using Support Vectorised Data and Intelligent Techniques
Kovuru Vijaya Kumar1, Ch GVN Prasad2
1Kovuru Vijaya Kumar*, Computer Science and Engineering Department, Rayalaseema University, Kurnool, Andhra Pradesh, India.
2Ch GVN Prasad, Department of Computer Science & Engineering, Sri Indu College of Engg & Tech, Hyderabad. India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 26, 2020. | Manuscript published on March 10, 2020. | PP: 73-79 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2124039520/2020©BEIESP | DOI: 10.35940/ijitee.E2124.039520
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Software enhances the working capability of any business. Developing such a software entrusts the developing organization to build defect free software. In this context we have used PC1 dataset(NASA dataset) which has sufficient parameters for analysis. Intelligent techniques using different methodologies have been applied exhaustively on the PC1 data to find out the best intelligent technique for software defect. As the PC1 data is highly imbalanced data, there was biasness in the prediction of the intelligent techniques. Hence, to overcome this issue, in this paper we tried to propose best balancing method along with the intelligent technique to predict the software defect accurately.
Keywords: Software Defect Prediction, Decision Tree (DT), Support Vectorised data, Logistic Regression(LR), Synthetic Minority Over-Sampling Ttechnique (SMOTE).
Scope of the Article: Software Engineering & Its Applications