An Evidential approach on Feature Subset Selection in Software Defect Prediction
DOI:
https://doi.org/10.26438/ijcse/v5i12.4149Keywords:
Software Defect, prediction, dempster shafer theory, probability, evidence, reliabilityAbstract
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.
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