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  1. University of Computer Studies, Yangon
  2. Journals

Comparison of Classification Methods on Software Defect Data Sets

http://hdl.handle.net/20.500.12678/0000004359
http://hdl.handle.net/20.500.12678/0000004359
2feb0659-e4af-4c66-be87-fd025ceab6c1
4f7aefb3-7adc-4200-8234-be36096e310d
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NJPSC NJPSC 2019 Proceedings-pages-68-74.pdf (650 Kb)
Publication type
Article
Upload type
Publication
Title
Title Comparison of Classification Methods on Software Defect Data Sets
Language en_US
Publication date 2019-03
Authors
San, Hnin Yi
Oo, Khine Khine
Description
Nowadays it is difficult for us to imagine a lifewithout devices that is controlled by software. Softwarequality prediction is the important process of softwaredevelopment processes. It is a process of utilizingsoftware metrics such as code-level measurements anddefect data to estimate software quality modules. Amore useful and efficient mechanism is k NearestNeighbor method to classify class of target data basedon k nearest training dataset. By applying the concept ofk-NN, we propose a new mechanism called Class BaseWeighted k-NN with Biner Algorithm (CBW k-NN) tofind the range of training dataset where the target datahas the maximum likelihood of occurrence by Biner andclassify class of target data based on this range. Themain purpose of this paper is to know the effectiveclassification method for software defect datasets thatexploit information from the NASA MDP (PC1, CM1,JM1) datasets.
Keywords
Biner, Class Based Weighted k Nearest Neighbor, Classification, k Nearest Neighbor, NASA MDP dataset, Software Defect Prediction
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/2298
Journal articles
National Journal of Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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