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Software Defect Prediction Using Principal Component Analysis and Naïve Bayes Algorithm

  • N. DhamayanthiEmail author
  • B. Lavanya
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 28)

Abstract

How can I deliver defect-free software? Can I achieve more with less resources? How can I reduce time, effort, and cost involved in developing software? Software defect prediction is an important area of research which can significantly help the software development teams grappling with these questions in an effective way. A small increase in prediction accuracy will go a long way in helping software development teams improve their efficiency. In this paper, we have proposed a framework which uses PCA for dimensionality reduction and Naïve Bayes classification algorithm for building the prediction model. We have used seven projects from NASA Metrics Data Program for conducting experiments. We have seen an average increase of 10.3% in prediction accuracy when the learning algorithm is applied with the key features extracted from the datasets.

Keywords

Software defect prediction Fault proneness Classification Feature selection Naïve Bayes classification algorithm Principal component analysis Software quality Machine learning algorithms Fault prediction Dimensionality reduction Data mining Machine learning techniques NASA Metrics Data Program Stratified tenfold cross-validation Reliable software Prediction modeling 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MadrasChennaiIndia

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