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Defect Severity-based Dimension Reduction Model using PCA

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2019, 15(1), pp.79-86
  • DOI : 10.29056/jsav.2019.06.09
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science
  • Received : May 31, 2019
  • Accepted : June 20, 2019
  • Published : June 30, 2019

Kwon, Ki Tae 1 Na-Young Lee 1

1강릉원주대학교

Candidate

ABSTRACT

Software dimension reduction identifies the commonality of elements and extracts important feature elements. So it reduces complexity by simplify and solves multi-collinearity problems. And it reduces redundancy by performing redundancy and noise detection. In this study, we proposed defect severity-based dimension reduction model. Proposed model is applied defect severity-based NASA dataset. And it is verified the number of dimensions in the column that affect the severity of the defect. Then it is compares and analyzes the dimensions of the data before and after reduction. In this study experiment result, the number of dimensions of PC4's dataset is 2 to 3. It was possible to reduce the dimension.

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