Table of Contents
ISRN Software Engineering
Volume 2014 (2014), Article ID 251083, 15 pages
http://dx.doi.org/10.1155/2014/251083
Research Article

Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis

Department of Computer Science and Engineering, National Institute of Technology, Rourkela, Odisha 769008, India

Received 31 August 2013; Accepted 16 January 2014; Published 4 March 2014

Academic Editors: K. Framling, Z. Shen, and S. K. Shukla

Copyright © 2014 Yeresime Suresh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. V. R. Basili, L. C. Briand, and W. L. Melo, “A validation of object-oriented design metrics as quality indicators,” IEEE Transactions on Software Engineering, vol. 22, no. 10, pp. 751–761, 1996. View at Publisher · View at Google Scholar · View at Scopus
  2. T. J. McCabe, “A Complexity Measure,” IEEE Transactions on Software Engineering, vol. 2, no. 4, pp. 308–320, 1976. View at Google Scholar · View at Scopus
  3. M. H. Halstead, Elements of Software Science, Elsevier Science, New York, NY, USA, 1977.
  4. W. Li and S. Henry, “Maintenance metrics for the Object-Oriented paradigm,” in Proceedings of the 1st International Software Metrics Symposium, pp. 52–60, 1993.
  5. S. R. Chidamber and C. F. Kemerer, “Metrics suite for object oriented design,” IEEE Transactions on Software Engineering, vol. 20, no. 6, pp. 476–493, 1994. View at Publisher · View at Google Scholar · View at Scopus
  6. F. B. E. Abreu and R. Carapuca, “Object-Oriented software engineering: measuring and controlling the development process,” in Proceedings of the 4th International Conference on Software Quality, pp. 1–8, McLean, Va, USA, October 1994.
  7. M. Lorenz and J. Kidd, Object-Oriented Software Metrics, Prentice Hall, Englewood, NJ, USA, 1994.
  8. R. Martin, “OO design quality metrics—an analysis of dependencies,” in Proceedings of the Workshop Pragmatic and Theoretical Directions in Object-Oriented Software Metrics (OOPSLA '94), 1994.
  9. D. P. Tegarden, S. D. Sheetz, and D. E. Monarchi, “A software complexity model of object-oriented systems,” Decision Support Systems, vol. 13, no. 3-4, pp. 241–262, 1995. View at Google Scholar · View at Scopus
  10. W. Melo and F. B. E. Abreu, “Evaluating the impact of object-oriented design on software quality,” in Proceedings of the 3rd International Software Metrics Symposium, pp. 90–99, Berlin, Germany, March 1996. View at Scopus
  11. L. Briand, P. Devanbu, and W. Melo, “Investigation into coupling measures for C++,” in Proceedings of the IEEE 19th International Conference on Software Engineering Association for Computing Machinery, pp. 412–421, May 1997. View at Scopus
  12. L. Etzkorn, J. Bansiya, and C. Davis, “Design and code complexity metrics for OO classes,” Journal of Object-Oriented Programming, vol. 12, no. 1, pp. 35–40, 1999. View at Google Scholar · View at Scopus
  13. L. C. Briand, J. Wüst, J. W. Daly, and D. Victor Porter, “Exploring the relationships between design measures and software quality in object-oriented systems,” The Journal of Systems and Software, vol. 51, no. 3, pp. 245–273, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. M.-H. Tang, M.-H. Kao, and M.-H. Chen, “Empirical study on object-oriented metrics,” in Proceedings of the 6th International Software Metrics Symposium, pp. 242–249, November 1999. View at Scopus
  15. K. El Emam, W. Melo, and J. C. Machado, “The prediction of faulty classes using object-oriented design metrics,” Journal of Systems and Software, vol. 56, no. 1, pp. 63–75, 2001. View at Google Scholar · View at Scopus
  16. T. M. Khoshgoftaar, E. B. Allen, J. P. Hudepohl, and S. J. Aud, “Application of neural networks to software quality modeling of a very large telecommunications system,” IEEE Transactions on Neural Networks, vol. 8, no. 4, pp. 902–909, 1997. View at Publisher · View at Google Scholar · View at Scopus
  17. R. Hochman, T. M. Khoshgoftaar, E. B. Allen, and J. P. Hudepohl, “Evolutionary neural networks: a robust approach to software reliability problems,” in Proceedings of the 8th International Symposium on Software Reliability Engineering (ISSRE '97), pp. 13–26, November 1997. View at Scopus
  18. T. Menzies, B. Caglayan, E. Kocaguneli, J. Krall, F. Peters, and B. Turhan, “The PROMISE Repository of empirical software engineering data,” West Virginia University, Department of Computer Science, 2012, http://promisedata.googlecode.com.
  19. Y. Kumar Jain and S. K. Bhandare, “Min max normalization based data perturbation method for privacy protection,” International Journal of Computer and Communication Technology, vol. 2, no. 8, pp. 45–50, 2011. View at Google Scholar
  20. R. Battiti, “First and Second-Order Methods for Learning between steepest descent and newton's method,” Neural Computation, vol. 4, no. 2, pp. 141–166, 1992. View at Publisher · View at Google Scholar
  21. K. Levenberg, “A method for the solution of certain non-linear problems in least squares,” Quarterly of Applied Mathematics, vol. 2, no. 2, pp. 164–168, 1944. View at Google Scholar
  22. D. W. Marquardt, “An algorithm for the lest-squares estimation of non-linear parameters,” SIAM Journal of Applied Mathematics, vol. 11, no. 2, pp. 431–441, 1963. View at Publisher · View at Google Scholar
  23. Y. H. Pao, Adaptive Pattern Recognition and Neural Networks, Addison-Wesley, Reading, UK, 1989.
  24. D. F. Specht, “Probabilistic neural networks,” Neural Networks, vol. 3, no. 1, pp. 109–118, 1990. View at Google Scholar · View at Scopus
  25. C. Catal, “Performance evaluation metrics for software fault prediction studies,” Acta Polytechnica Hungarica, vol. 9, no. 4, pp. 193–206, 2012. View at Google Scholar
  26. X. Yaun, T. M. Khoshgoftaar, E. B. Allen, and K. Ganesan, “Application of fuzzy clustering to software quality prediction,” in Proceedings of the 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology (ASSEST '00), pp. 85–91, March 2000.
  27. T. Gyimóthy, R. Ferenc, and I. Siket, “Empirical validation of object-oriented metrics on open source software for fault prediction,” IEEE Transactions on Software Engineering, vol. 31, no. 10, pp. 897–910, 2005. View at Publisher · View at Google Scholar · View at Scopus
  28. G. Denaro, M. Pezzè, and S. Morasca, “Towards industrially relevant fault-proneness models,” International Journal of Software Engineering and Knowledge Engineering, vol. 13, no. 4, pp. 395–417, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Kanmani and U. V. Rymend, “Object-Oriented software quality prediction using general regression neural networks,” SIGSOFT Software Engineering Notes, vol. 29, no. 5, pp. 1–6, 2004. View at Publisher · View at Google Scholar
  30. N. Nagappan and W. Laurie, “Early estimation of software quality using in-process testing metrics: a controlled case study,” in Proceedings of the 3rd Workshop on Software Quality, pp. 1–7, St. Louis, Mo, USA, 2005.
  31. H. M. Olague, L. H. Etzkorn, S. Gholston, and S. Quattlebaum, “Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly Iterative or agile software development processes,” IEEE Transactions on Software Engineering, vol. 33, no. 6, pp. 402–419, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. K. K. Aggarwal, Y. Singh, A. Kaur, and R. Malhotra, “Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: a replicated case study,” Software Process Improvement and Practice, vol. 14, no. 1, pp. 39–62, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. F. Wu, “Empirical validation of object-oriented metrics on NASA for fault prediction,” in Proceedings of theInternational Conference on Advances in Information Technology and Education, pp. 168–175, 2011.
  34. H. Kapila and S. Singh, “Analysis of CK metrics to predict software fault-proneness using bayesian inference,” International Journal of Computer Applications, vol. 74, no. 2, pp. 1–4, 2013. View at Google Scholar