文章基本信息

标题：Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine
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作者：Udoinyang G. Inyang ; Olufemi S. Adeoye ; Edward N. Udo 等
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2022
卷号：15
期号：3
页码：47-60
DOI：10.5539/cis.v15n3p47
语种：English
出版社：Canadian Center of Science and Education
摘要：In today's society, almost all human endeavours depend on software products. Lack of quality software is one of the software industry's most important problems. Hence, it would be beneficial to access the quality of software to improve and enhance software products while increasing customer satisfaction. This paper assesses software product quality using a Support Vector Machine-based ensemble classifier. The ISO/IEC-9126 (International Organization for Standardization 2001) software quality (SQ) framework was adopted in this work. Dimension reduction of the product metric category dataset and the entire PM dataset was conducted using linear discriminant analysis (LDA). SVM kernel functions (linear, quadratic, cubic, fine gaussian, medium gaussian and coarse gaussian) were used to model each classifier. The combinations of the results from the multiple SVMs used AdaBoost, bagging, and random subspace ensemble methods for the assessment of SQ. All three ensemble learning methods performed better than the individual SVM, however, the bagging stood out with an accuracy of 93.0%. Hence, it was adopted in the fusion of the SVM results and classification of SQ into classes. Results from the confusion matrix and receivers’ operating characteristics were greater than 97.99% and confirm significant improvements with an ensemble of homogenous classifiers based on SVM.
关键词：support vector machine;ensemble learning;software quality;software defect prediction;SVMKernel