|An Evidential approach on Feature Subset Selection in Software Defect Prediction|
|M. Jaikumar1 , V. Kathiresan2|
1 Department of Computer Applications, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, India .
2 Department of Computer Applications, Dr.SNS Rajalakshmi College of Arts and Science, Coimbatore, India.
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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 41-49, Dec-2017
Online published on Dec 31, 2017
Copyright © M. Jaikumar , V. Kathiresan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: M. Jaikumar , V. Kathiresan, “An Evidential approach on Feature Subset Selection in Software Defect Prediction”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.41-49, 2017.
MLA Style Citation: M. Jaikumar , V. Kathiresan "An Evidential approach on Feature Subset Selection in Software Defect Prediction." International Journal of Computer Sciences and Engineering 5.12 (2017): 41-49.
APA Style Citation: M. Jaikumar , V. Kathiresan, (2017). An Evidential approach on Feature Subset Selection in Software Defect Prediction. International Journal of Computer Sciences and Engineering, 5(12), 41-49.
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|In software quality research, software defect is a key topic. The characteristic of software attributes influences the performance and effectiveness of the defect prediction model. However this issue is not well explored to the best of our knowledge. So this paper focus on the problem of attribute selection in the context of software defect prediction, we propose a Dempster-Shafer Theory technique with modified combination rule known as Dubois And Prade’s Disjunctive Consensus Rule is adapted for selecting best set of attributes to improve the accuracy of the software defect prediction. Dempster-Shafer Theory (DST) offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The proposed method is evaluated using the data sets from NASA metric data repository.|
|Key-Words / Index Term :|
|Software Defect, prediction, dempster shafer theory, probability, evidence, reliability|
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