Proposed software faults detection using hybrid approach.

The major challenge is to validate software failure dataset by finding unknown model parameters used. For software assurance, previously many attempts were made based using classical classifiers as decision tree, Naïve Bayes, and k‐nearest neighbor for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect‐free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on particle swarm optimization and modified genetic algorithm for feature selection and bagging for effective classification of defective or nondefective modules in a dataset. This paper presents an empirical study on National Aeronautics and Space Administration Metric Data Program datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods. [ABSTRACT FROM AUTHOR]