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
相关论文
共 50 条
  • [1] LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data
    Munehiro Nakamura
    Yusuke Kajiwara
    Atsushi Otsuka
    Haruhiko Kimura
    BioData Mining, 6
  • [2] Software defect prediction using learning to rank approach
    Nassif, Ali Bou
    Talib, Manar Abu
    Azzeh, Mohammad
    Alzaabi, Shaikha
    Khanfar, Rawan
    Kharsa, Ruba
    Angelis, Lefteris
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [3] Software defect prediction using learning to rank approach
    Ali Bou Nassif
    Manar Abu Talib
    Mohammad Azzeh
    Shaikha Alzaabi
    Rawan Khanfar
    Ruba Kharsa
    Lefteris Angelis
    Scientific Reports, 13
  • [4] LVQ-SMOTE - Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data
    Nakamura, Munehiro
    Kajiwara, Yusuke
    Otsuka, Atsushi
    Kimura, Haruhiko
    BIODATA MINING, 2013, 6
  • [5] The Classification of Fetus Gender on Ultrasound Images Using Learning Vector Quantization (LVQ)
    Maysanjaya, I. Md Dendi
    Nugroho, Hanung Adi
    Setiawan, Noor Akhmad
    2014 MAKASSAR INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS (MICEEI), 2014, : 150 - 155
  • [6] Hybridization of Learning Vector Quantization (LVQ) and Adaptive Coordinates (AC) for data classification and visualization
    Tapan, Sarwar Zahan
    Teh, Chee Siong
    ICIAS 2007: INTERNATIONAL CONFERENCE ON INTELLIGENT & ADVANCED SYSTEMS, VOLS 1-3, PROCEEDINGS, 2007, : 505 - 510
  • [7] Enriched Hybrid Recursive Feature Elimination Algorithm with Learning Vector Quantization Classifier for Heterogenous Cross Project Defect Prediction
    Lakshmi, J. Deepa
    Chandran, M.
    JOURNAL OF ALGEBRAIC STATISTICS, 2022, 13 (02) : 410 - 420
  • [8] Handling Imbalanced Data using Ensemble Learning in Software Defect Prediction
    Malhotra, Ruchika
    Jain, Juhi
    PROCEEDINGS OF THE CONFLUENCE 2020: 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING, 2020, : 300 - 304
  • [9] Exhaustive and heuristic search approaches for learning a software defect prediction model
    Pendharkar, Parag C.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2010, 23 (01) : 34 - 40
  • [10] Active learning using uncertainty sampling and query-by-committee for software defect prediction
    Qu Y.
    Chen X.
    Chen R.
    Ju X.
    Guo J.
    International Journal of Performability Engineering, 2019, 15 (10): : 2701 - 2708