Yukun Bao, Dongbo Yi, and Junhua Chen


  1. [1] J.D. Musa, A. Iannino, and K. Okumoto, Software reliability:Measurement, prediction, application (New York: McGraw-Hill, 1987).109
  2. [2] M. Xie, Software reliability modeling (Singapore: World Sci-entific, 1991).
  3. [3] M.R. Lyu, Handbook of software reliability engineering (NewYork: McGraw-Hill, 1996).
  4. [4] N. Karunanithi, D. Whitley, and Y.K. Malaiya, Prediction ofsoftware reliability using connectionist models, IEEE Trans-action on Software Engineering, 18(7), 1992, 563–573.
  5. [5] N. Karunanithi, D. Whitley, and Y.K Malaiya, Using neuralnetworks in reliability prediction, IEEE Software, 9(4), 1992,53–59.
  6. [6] J.Y. Park, S.U. Lee, and J.H. Park, Neural network modeling forsoftware reliability prediction from failure time data, Journalof Electrical Engineering and Information Science, 4(4), 1999,533–538.
  7. [7] W.A. Adnan, M. Yaakob, R. Anas, and M.R. Tamjis, Artificialneural network for software reliability assessment, Proc. of2000 TENCON: Intelligent Systems and Technologies for theNew Millennium, Kuala Lumpur, Malaysia, 2000, 446–451.
  8. [8] S.H. Aljahdali, A. Sheta, and D. Rine, Prediction of soft-ware reliability: A comparison between regression and neuralnetwork non-parametric models, Proc. ACS/IEEE Interna-tional Conf. on Computer Systems and Applications, Beirut,Lebanon, 2001, 470–473.
  9. [9] K.Y. Cai, L. Cai, W.D. Wang, Z.Y. Yu, and D. Zhang, Onthe neural network approach in software reliability modeling,Journal of Systems and Software, 58(1), 2001, 47–62.
  10. [10] L. Tian and A. Noore, Evolutionary neural network modelingfor software cumulative failure time prediction, ReliabilityEngineering and System Safety, 87(1), 2005, 45–51.
  11. [11] Q.P. Hu, M. Xie, S.H. Ng, and G. Levitin, Robust recurrentneural network modeling for software fault detection and cor-rection prediction, Reliability Engineering and System Safety,92(3), 2007, 332–340.
  12. [12] N.R. Kiran and V. Ravi, Software reliability prediction bysoft computing techniques, Journal of Systems and Software,81(4), 2008, 576–583.
  13. [13] N.R. Kiran and V. Ravi, Software reliability prediction us-ing wavelet neural networks, Proc. IEEE International Conf.on Computational Intelligence and Multimedia Applications,Sivakasi, India, 2007, 195–199.
  14. [14] P.F. Pai, System reliability forecasting by support vectormachines with genetic algorithms, Mathematical and ComputerModeling, 43, 2006, 262–274.
  15. [15] P.F. Pai and W.C. Hong, Software reliability forecasting bysupport vector machines with simulated annealing algorithms,Journal of Systems and Software, 79(6), 2006, 747–755.
  16. [16] S.L. Ho and M. Xie, The use of ARIMA models for reliabilityforecasting and analysis, Computers and Industrial Engineer-ing, 35, 1998, 213–216.
  17. [17] S.L. Ho, M. Xie, and T.N. Goh, A comparative study ofneural network and Box-Jenkins ARIMA modeling in timeseries forecasting, Computers and Industrial Engineering, 42,2002, 371–375.
  18. [18] Z. Wu and N.E. Huang, Ensemble empirical mode decomposi-tion: A noise-assisted data analysis method, COLA TechnicalReport 193, 2005, ctr_193.pdf.
  19. [19] N.E. Huang, Z. Shen, and S.R. Long, The empirical modedecomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of Royal Society,London 454A, 1998, 903–995.
  20. [20] J.D. Musa, Software Reliability Data (New York: IEEE Com-puter Society Press, 1979).

Important Links:

Go Back