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)


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.


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


  1. 1.
    S. Chen, Z. Daoqiang, Semisupervised Dimensionality Reduction With Pairwise Constraints for Hyperspectral Image Classification. IEEE Geosci. Remote Sens. Lett. 8(2), 369–373 (Mar 2011), Scholar
  2. 2.
    Z. Chen, T. Menzies, D. Port, D. Boehm, Finding the right data for software cost modeling. IEEE Softw. 22(6), 38–46 (2005)CrossRefGoogle Scholar
  3. 3.
    S.R. Chidamber, C.F. Kemerer, A metrics suite for object oriented design. IEEE Trans. Softw. Eng. 20(6), 476–493 (1994)CrossRefGoogle Scholar
  4. 4.
    I. Guyon, A. Elisseeff, An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)zbMATHGoogle Scholar
  5. 5.
    B. Henderson-Sellers, Object-Oriented Metrics: Measures of Complexity (Prentice-Hall, Inc., Englewood Cliffs, 1995)Google Scholar
  6. 6.
    A.G. Koru, H. Liu, Building effective defect-prediction models in practice. IEEE Softw. 22(6), 23–29 (2005)CrossRefGoogle Scholar
  7. 7.
    D. Lakshmipadmaja, B. Vishnuvardhan, Classification performance improvement using random subset feature selection algorithm for data mining. Big Data Res. (2018)Google Scholar
  8. 8.
    R. Martin, OO design quality metrics. Anal. Depend. 12, 151–170 (1994)Google Scholar
  9. 9.
    D.L. Padmaja, B. Vishnuvardhan, Comparative study of feature subset selection methods for dimensionality reduction on scientific data, in 2016 IEEE 6th International Conference on Advanced Computing (IACC) (IEEE, New York, 2016), pp. 31–34Google Scholar
  10. 10.
    N. Painter, B. Kadhiwala, Comparative analysis of android malware detection techniques, in Proceedings of the International Conference on Data Engineering and Communication Technology (Springer, 2017), pp. 131–139Google Scholar
  11. 11.
    A. Panhalkar, D. Doye, An outlook in some aspects of hybrid decision tree classification approach: a survey, in Proceedings of the International Conference on Data Engineering and Communication Technology (Springer, 2017), pp. 85–95Google Scholar
  12. 12.
    I. Rodriguez-Lujan, R. Huerta, C. Elkan, C. S. Cruz, Quadratic programming feature selection. J. Mach. Learn. Res.11(3), (1532–4435) 1491–1516, (1859900), (Aug 2010),
  13. 13.
    M.H. Tang, M.H. Kao, M.H. Chen, An empirical study on object-oriented metrics, in Proceedings Sixth International Software Metrics Symposium 1999 (IEEE, New York, 1999), pp. 242–249Google Scholar
  14. 14.
    D. Wahyudin, R. Ramler, S. Biffl, A framework for defect prediction in specific software project contexts, in IFIP Central and East European Conference on Software Engineering Techniques (Springer, Berlin, 2008), pp. 261–274CrossRefGoogle Scholar
  15. 15.
    E.J. Weyuker, T.J. Ostrand, R.M. Bell, Do too many cooks spoil the broth? using the number of developers to enhance defect prediction models. Empir. Softw. Eng. 13(5), 539–559 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • G. N. V. Ramana Rao
    • 1
    Email author
  • 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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