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DOI: 10.14569/IJACSA.2021.0120219
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An Hybrid Approach for Cost Effective Prediction of Software Defects

Author 1: Satya Srinivas Maddipati
Author 2: Malladi Srinivas

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 2, 2021.

  • Abstract and Keywords
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Abstract: Identifying software defects during early stages of Software Development life cycle reduces the project effort and cost. Hence there is a lot of research done in finding defective proneness of a software module using machine learning approaches. The main problems with software defect data are cost effective and imbalance. Cost effective problem refers to predicting defective module as non defective induces high penalty compared to predicting non defective module as defective. In our work, we are proposing a hybrid approach to address cost effective problem in Software defect data. To address cost effective problem, we used bagging technique with Artificial Neuro Fuzzy Inference system as base classifier. In addition to that, we also addressed Class Imbalance & High dimensionality problems using Artificial Neuro Fuzzy inference system & principle component analysis respectively. We conducted experiments on software defect datasets, downloaded from NASA dataset repository using our proposed approach and compared with approaches mentioned in literature survey. We observed Area under ROC curve (AuC) for proposed approach was improved approximately 15% compared with highly efficient approach mentioned in literature survey.

Keywords: Cost effective problem; principle component analysis; adaptive neuro fuzzy inference system; area under ROC curve

Satya Srinivas Maddipati and Malladi Srinivas, “An Hybrid Approach for Cost Effective Prediction of Software Defects” International Journal of Advanced Computer Science and Applications(IJACSA), 12(2), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120219

@article{Maddipati2021,
title = {An Hybrid Approach for Cost Effective Prediction of Software Defects},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120219},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120219},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Satya Srinivas Maddipati and Malladi Srinivas}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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