Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (8): 2049-2061.doi: 10.23940/ijpe.19.08.p4.20492061

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Proposed Hybrid Approach to Predict Software Fault Detection

Manu Banga, Abhay Bansal, and Archana Singh*   

  1. ASET, Amity University, Noida, 201313, India
  • Received:2019-06-22 Online:2019-08-20 Published:2019-09-10
  • Contact: * E-mail address: archana.elina@gmail.com

Abstract: The major challenge is to validate software failure dataset by finding unknown model parameters used. Previously, many attempts for software assurance were made using classical classifiers as Decision Tree, Naïve Bayes, and k-NN 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 (PSO) & Modified Genetic Algorithm (MGA) for feature selection and Bagging for effective classification of defective or non-defective modules in a dataset. This paper presents an empirical study on NASA Metric Data Program (MDP) datasets, using the proposed hybrid algorithm. Results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods.

Key words: software reliability, software fault classification, model parameter estimation, modified genetic algorithm, support vector machines, particle swarm optimization