Data sampling approach using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction

被引:3
|
作者
Amanullah, M. [1 ]
Ramya, S. Thanga [2 ]
Sudha, M. [3 ]
Pushparathi, V. P. Gladis [4 ]
Haldorai, Anandakumar [5 ]
Pant, Bhaskar [6 ]
机构
[1] Aalim Muhammad Salegh Coll Engn, Dept Informat Technol, Chennai, Tamil Nadu, India
[2] RMK Engn Coll, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India
[3] SASTRA Deemed Be Univ, Srinivasa Ramanujan Ctr, Dept Elect & Commun, Kumbakonam, India
[4] Velammal Inst Technol, Dept Comp Sci & Engn, Chennai, Tamil Nadu, India
[5] Sri Eshwar Coll Engn, Dept Comp Sci & Engn, Coimbatore, Tamil Nadu, India
[6] Graph Era Deemed Be Univ, Dept Comp Sci & Engn, Bell Rd, Dehra Dun, Uttarakhand, India
关键词
Software defect prediction; improved random-SMOTE oversampling technique; linear pearson correlation; heuristic learning vector quantization (LVQ); training and test datasets;
D O I
10.3233/JIFS-220480
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:3867 / 3876
页数:10
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