Abstract: The major challenge in designing an Intelligent Information Software System for fault detection is to detect faults at an early stage unless it becomes a failure. This can be achieved by using feature selection and effective classification applied on failure datasets. Support Vector Machines (SVM) are used for efficient and accurate feature selection by finding unknown model parameters using local and global kernel parameter optimization. Research shows that previously many attempts were made using classical classifiers as decision tree, naïve bayes, and k-NN for software fault prediction. In earlier research, class imbalance problems in software fault datasets were not addressed. In this paper, we propose an intelligent hybrid algorithm that is based on feature selection hybrid kernel function SVM and entropy-based bagging for efficient classification to reduce the class imbalance problem. The proposed model is compared with traditional approaches. The improved hybrid algorithm based on entropy-based bagging and mixed kernel SVM can effectively improve the classification accuracy of NASA Metric Data Program (MDP) faulty datasets. This paper presents an empirical study on using the proposed hybrid algorithm and results showed that our proposed approach enhances the classification accuracy when compared with existing methods.

Key words: software fault classification, model parameter estimation, support vector machines, entropy, bagging