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Attribute Reduction for Defect Prediction Using Random Subset Feature Selection Method

  • G. N. V. Ramana RaoEmail author
  • V. V. S. S. S. Balaram
  • B. Vishnuvardhan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)

Abstract

Large software products require high effort to maintain the code base. Most of the time managers face challenging situations for efficient allocation of resources. In this paper, we proposed a novel approach to aid the software engineering managers to predict the software defects using few matrices. In our study, we have used publicly available software engineering repositories concentrating on object-oriented (OO) methodology. Our study suggests that few important matrices are sufficient to predict the defects in the system. We have used kNN classifier for classification and random subset feature selection (RSFS) for dimensionality reduction of the attributes.

Keywords

Software defect prediction Object-oriented metrics Feature subset selection Dimensionality reduction Software engineering 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • G. N. V. Ramana Rao
    • 1
  • V. V. S. S. S. Balaram
    • 2
  • B. Vishnuvardhan
    • 3
  1. 1.Wipro LtdBengaluruIndia
  2. 2.Department of ITSNISTHyderabadIndia
  3. 3.Department of Computer ScienceJNTUHHyderabadIndia

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