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Comparison of Classification Methods on Software Defect Data Sets

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dc.contributor.author San, Hnin Yi
dc.contributor.author Oo, Khine Khine
dc.date.accessioned 2019-10-15T16:15:27Z
dc.date.available 2019-10-15T16:15:27Z
dc.date.issued 2019-03
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/2298
dc.description.abstract Nowadays it is difficult for us to imagine a life without devices that is controlled by software. Software quality prediction is the important process of software development processes. It is a process of utilizing software metrics such as code-level measurements and defect data to estimate software quality modules. A more useful and efficient mechanism is k Nearest Neighbor method to classify class of target data based on k nearest training dataset. By applying the concept of k-NN, we propose a new mechanism called Class Base Weighted k-NN with Biner Algorithm (CBW k-NN) to find the range of training dataset where the target data has the maximum likelihood of occurrence by Biner and classify class of target data based on this range. The main purpose of this paper is to know the effective classification method for software defect datasets that exploit information from the NASA MDP (PC1, CM1, JM1) datasets. en_US
dc.language.iso en_US en_US
dc.publisher National Journal of Parallel and Soft Computing en_US
dc.relation.ispartofseries Vol-1, Issue-1;
dc.subject Biner en_US
dc.subject Class Based Weighted k Nearest Neighbor en_US
dc.subject Classification en_US
dc.subject k Nearest Neighbor en_US
dc.subject NASA MDP dataset en_US
dc.subject Software Defect Prediction en_US
dc.title Comparison of Classification Methods on Software Defect Data Sets en_US
dc.type Article en_US

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