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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 312067, 21 pages
http://dx.doi.org/10.1155/2013/312067
Research Article

Empirical Study of Homogeneous and Heterogeneous Ensemble Models for Software Development Effort Estimation

1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
2College of Engineering, Tanta University, Tanta, Egypt

Received 13 March 2013; Revised 3 June 2013; Accepted 9 June 2013

Academic Editor: Ren-Jieh Kuo

Copyright © 2013 Mahmoud O. Elish 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. J. Wen, S. Li, Z. Lin, Y. Hu, and C. Huang, “Systematic literature review of machine learning based software development effort estimation models,” Information and Software Technology, vol. 54, no. 1, pp. 41–59, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. R. N. Charette, “Why software fails,” IEEE Spectrum, vol. 42, no. 9, pp. 36–43, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. C. López-Martín, C. Yáñez-Márquez, and A. Gutiérrez-Tornés, “Predictive accuracy comparison of fuzzy models for software development effort of small programs,” Journal of Systems and Software, vol. 81, no. 6, pp. 949–960, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. I. F. de Barcelos Tronto, J. D. S. da Silva, and N. Sant'Anna, “An investigation of artificial neural networks based prediction systems in software project management,” Journal of Systems and Software, vol. 81, no. 3, pp. 356–367, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Grimstad and M. Jørgensen, “Inconsistency of expert judgment-based estimates of software development effort,” Journal of Systems and Software, vol. 80, no. 11, pp. 1770–1777, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Jørgensen, “A review of studies on expert estimation of software development effort,” Journal of Systems and Software, vol. 70, no. 1-2, pp. 37–60, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Boehm, Software Engineering Economics, Prentice-Hall, New Jersey, NJ, USA, 1981.
  8. A. J. Albrecht and J. E. Gaffney Jr., “Software function, source lines of code and development effort prediction: a software science validation,” IEEE Transactions on Software Engineering, vol. 9, no. 6, pp. 639–648, 1983. View at Google Scholar · View at Scopus
  9. L. H. Putnam, “A general empirical solution to the macro sizing and estimating problem,” IEEE Transactions on Software Engineering, vol. 4, no. 4, pp. 345–361, 1978. View at Google Scholar · View at Scopus
  10. A. Idri, A. Abran, and T. Khoshgoftaar, “Estimating software project effort by analogy based on linguistic values,” in Proceedings of the 8th IEEE Symposium on Software Metrics, pp. 21–30, 2002.
  11. A. Heiat, “Comparison of artificial neural network and regression models for estimating software development effort,” Information and Software Technology, vol. 44, no. 15, pp. 911–922, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. E. S. Jun and J. K. Lee, “Quasi-optimal case-selective neural network model for software effort estimation,” Expert Systems with Applications, vol. 21, no. 1, pp. 1–14, 2001. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Shin and A. L. Goel, “Empirical data modeling in software engineering using radial basis functions,” IEEE Transactions on Software Engineering, vol. 26, no. 6, pp. 567–576, 2000. View at Publisher · View at Google Scholar · View at Scopus
  14. P. C. Pendharkar, G. H. Subramanian, and J. A. Rodger, “A probabilistic model for predicting software development effort,” IEEE Transactions on Software Engineering, vol. 31, no. 7, pp. 615–624, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. B. Başkeleş, B. Turhan, and A. Bener, “Software effort estimation using machine learning methods,” in Proceedings of the 22nd International Symposium on Computer and Information Sciences (ISCIS '07), pp. 209–214, November 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Srinivasan and D. Fisher, “Machine learning approaches to estimating software development effort,” IEEE Transactions on Software Engineering, vol. 21, no. 2, pp. 126–137, 1995. View at Publisher · View at Google Scholar · View at Scopus
  17. N.-H. Chiu and S.-J. Huang, “The adjusted analogy-based software effort estimation based on similarity distances,” Journal of Systems and Software, vol. 80, no. 4, pp. 628–640, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Shepperd and C. Schofield, “Estimating software project effort using analogies,” IEEE Transactions on Software Engineering, vol. 23, no. 12, pp. 736–743, 1997. View at Publisher · View at Google Scholar · View at Scopus
  19. C. J. Burgess and M. Lefley, “Can genetic programming improve software effort estimation? A comparative evaluation,” Information and Software Technology, vol. 43, no. 14, pp. 863–873, 2001. View at Publisher · View at Google Scholar · View at Scopus
  20. K. K. Shukla, “Neuro-genetic prediction of software development effort,” Information and Software Technology, vol. 42, no. 10, pp. 701–713, 2000. View at Publisher · View at Google Scholar · View at Scopus
  21. A. L. I. Oliveira, “Estimation of software project effort with support vector regression,” Neurocomputing, vol. 69, no. 13-15, pp. 1749–1753, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. P. L. Braga, A. L. I. Oliveira, G. H. T. Ribeiro, and S. R. L. Meira, “Bagging predictors for estimation of software project effort,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '07), pp. 1595–1600, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Kultur, B. Turhan, and A. B. Bener, “ENNA: software effort estimation using ensemble of neural networks with associative memory,” in Proceedings of the 16th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (SIGSOFT '08/FSE-16), pp. 330–338, November 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Kultur, B. Turhan, and A. Bener, “Ensemble of neural networks with associative memory (ENNA) for estimating software development costs,” Knowledge-Based Systems, vol. 22, no. 6, pp. 395–402, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Minku and X. Yao, “Ensembles and locality: insight on improving software effort estimation,” Information and Software Technology, vol. 55, no. 8, pp. 1512–1528, 2013, 095058. View at Google Scholar
  26. L. L. Minku and X. Yao, “A principled evaluation of ensembles of learning machines for software effort estimation,” in Proceedings of the 7th International Conference on Predictive Models in Software Engineering (PROMISE '11), September 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. E. Kocaguneli, Y. Kultur, and A. Bene, “Combining multiple learners induced on multiple datasets for software effort prediction,” in Proceedings of the International Symposium on Software Reliability Engineering (ISSRE '09), 2009.
  28. E. Kocaguneli, T. Menzies, and J. W. Keung, “On the value of ensemble effort estimation,” IEEE Transactions on Software Engineering, vol. 38, no. 6, pp. 1403–1416, 2012. View at Google Scholar
  29. M. Elish, “Assessment of voting ensemble for estimating software development effort,” in Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM '13), pp. 322–327, 2013.
  30. T. Helmy and A. Fatai, “Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs,” International Journal of Computational Intelligence and Applications, vol. 9, no. 4, pp. 313–337, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. R. Harris, X. Yao, G. Brown, and J. Wyatt, “Diversity creation methods: a survey and categorisation,” Information Fusion, vol. 6, no. 1, pp. 5–20, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. N. Ueda and R. Nakano, “Generalization error of ensemble estimators,” in Proceedings of the IEEE International Conference on Neural Networks (ICNN '96), pp. 90–95, June 1996. View at Scopus
  33. Y. Zhao, J. Gao, and X. Yang, “A survey of neural network ensembles,” in Proceedings of the International Conference on Neural Networks and Brain Proceedings (ICNNB '05), pp. 438–442, October 2005. View at Scopus
  34. Y. Miyazaki, M. Terakado, K. Ozaki, and H. Nozaki, “Robust regression for developing software estimation models,” The Journal of Systems and Software, vol. 27, no. 1, pp. 3–16, 1994. View at Google Scholar · View at Scopus
  35. K. Maxwell, Applied Statistics For Software Managers, Prentice-Hall, New Jersey, NJ, USA, 2002.
  36. J. Desharnais, Analyse statistique de la productivitie des projets informatique a partie de la technique des point des fonction [M.S. thesis], University of Montreal, 1989.
  37. R. D. A. Araújo, A. L. I. de Oliveira, and S. C. B. Soares, “A morphological-rank-linear approach for software development cost estimation,” in Proceedings of the 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI '09), pp. 630–636, November 2009. View at Publisher · View at Google Scholar · View at Scopus