Software Defect Classification with a Variant of NSGA-II and Simple Voting Strategies

  • Emil Rubinić
  • Goran Mauša
  • Tihana Galinac Grbac
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9275)

Abstract

Software Defect Prediction is based on datasets that are imbalanced and therefore limit the use of machine learning based classification. Ensembles of genetic classifiers indicate good performance and provide a promising solution to this problem. To further examine this solution, we performed additional experiments in that direction. In this paper we report preliminary results obtained by using a Matlab variant of NSGA-II in combination with four simple voting strategies on three subsequent releases of the Eclipse Plug-in Development Environment (PDE) project. Preliminary results indicate that the voting procedure might influence software defect prediction performances.

Keywords

SDP SBSE Multi-objective optimisation NSGA-II 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Emil Rubinić
    • 1
  • Goran Mauša
    • 1
  • Tihana Galinac Grbac
    • 1
  1. 1.Faculty of EngineeringUniversity of RijekaRijekaCroatia

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