Affiliations: [a] Department of Information Technology, Aalim Muhammad Salegh College of Engineering, Chennai, India
| [b] Department of Computer Science and Engineering, R.M.K. Engineering College, Kavaraipettai, Chennai, India
| [c] Department of Electronics and Communication, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam, India
| [d] Department of Computer Science and Engineering, Velammal Institute of Technology, Pancheeti, Chennai, India
| [e] Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India
| [f] Department of Computer Science and Engineering, Graphic Era Deemed to be University, Bell Road, Clement Town, Dehradun, Uttarakhand, India
Correspondence:
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Corresponding author. M. Amanullah, Associate Professor, Department of Information Technology, Aalim Muhammad Salegh College of Engineering, Chennai, India. E-mail: amanullahm9797@gmail.com.
Abstract: On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist.
Keywords: Index Terms: Software defect prediction, improved random-SMOTE oversampling technique, linear pearson correlation, heuristic learning vector quantization (LVQ), training and test datasets
