Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine

In today's society, almost all human endeavours depend on software products. Lack of quality is one the industry's most important problems. Hence, it would be beneficial to access improve and enhance products while increasing customer satisfaction. This paper assesses product using a Support Vector Machine-based ensemble classifier. The ISO/IEC-9126 (International Organization for Standardization 2001) (SQ) framework was adopted in this work. Dimension reduction metric category dataset entire PM conducted linear discriminant analysis (LDA). SVM kernel functions (linear, quadratic, cubic, fine gaussian, medium gaussian coarse gaussian) were used model each combinations results from multiple SVMs AdaBoost, bagging, random subspace methods assessment SQ. All three learning performed better than individual SVM, however, bagging stood out with an accuracy 93.0%. fusion classification SQ into classes. Results confusion matrix receivers’ operating characteristics greater 97.99% confirm significant improvements homogenous classifiers based SVM.