Proposed approach to predict software faults detection using Entropy

Manu Banga (), Abhay Bansal () and Archana Singh ()
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Manu Banga: Amity University
Abhay Bansal: Amity University
Archana Singh: Amity University

International Journal of System Assurance Engineering and Management, 2020, vol. 11, issue 2, No 18, 312 pages

Abstract: Abstract 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-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 and modified genetic algorithm 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 datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods.

Keywords: Software reliability; Software fault classification; Model parameter estimation; Modified genetic algorithm; Support vector machines; Particle swarm optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-019-00934-2

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