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Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier

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  • Mrutyunjaya Panda

    (Utkal University, Odisha, India)

Abstract

Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classification method (DBBRBF) by combining distribution base balance (DBB) based instance selection and radial basis function (RBF) neural network classifier to obtain the best prediction compared to the existing research. The experimental results with post-hoc statistical significance tests shows the effectiveness of the proposed approach.

Suggested Citation

  • Mrutyunjaya Panda, 2019. "Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 8(3), pages 53-75, July.
    • Handle: RePEc:igg:jsda00:v:8:y:2019:i:3:p:53-75
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    Cited by:

    1. Abha Jain & Ankita Bansal, 2022. "Models for Efficient Utilization of Resources for Upgrading Android Mobile Technology," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 11(2), pages 1-22, August.
    2. Deepti Aggarwal & Sonu Mittal & Vikram Bali, 2021. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 38-49, July.

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