The Transactions of the Korea Information Processing Society (한국정보처리학회논문지)

Korea Information Processing Society (한국정보처리학회)
권호 표지

Software Reliability Prediction Using Predictive Filter

예측필터를 이용한 소프트웨어 신뢰성 예측

  • 박중양 (경상대학교 통계학과) ;
  • 이상운 (경상대학교 대학원 전자계산학과) ;
  • 박재흥 (경상대학교 컴퓨터과학과)
  • Published : 2000.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Almost all existing software reliability models are based on the assumptions of he software usage and software failure process. There, therefore, is no universally applicable software reliability model. To develop a universal software reliability model this paper suggests the predictive filter as a general software reliability prediction model for time domain failure data. Its usefulness is empirically verified by analyzing the failure datasets obtained from 14 different software projects. Based on the average relative prediction error, the suggested predictive filter is compared with other well-known neural network models and statistical software reliability growth models. Experimental results show that the predictive filter generally results in a simple model and adapts well across different software projects.

Keywords

References

  1. A. A. Abdel-Ghaly, P. Y. Chan and B. Littlewood, 'Evaluation of Competing ]Software Reliability Predictions,' IEEE Trans. Software Eng., Vol.SE-12, pp.950-967, 1986
  2. B. M. Anna-Mary, 'A Study of the Musa Reliability Model,' M.S. dissertation, Univ. Maryland, 1980
  3. S. Brocklehurst, B. Y. Chan, B. Littlewood and J. Snell, 'Recalibrating Software Reliability Models,' IEEE Trans. Software Eng., Vol.16, pp.458-470, 1990 https://doi.org/10.1109/32.54297
  4. D. S. Clouse, C. L. Giles, B. G. Home, and G. W. Cottrell, 'Time-Delay Neural Networks : Representation and Induction of Finite State Machines,' IEEE Trans. on Neural Networks, Vol.8, No.5, pp. 1065-1070, 1997 https://doi.org/10.1109/72.623208
  5. H. Demuth and M. Beale, 'Neural Network Toolbox User's Guide, Ver. 3.0,' Math Works, Inc. 1997
  6. A. L. Goel, 'Software Reliability Models : Assumptions, Limitations, and Applicability,' IEEE Trans. on Software Eng., Dec. pp.1411-1423, 1985
  7. N. Karunanithi, D. Whitley and Y. K. Malaiya, 'Prediction of Software Reliability Using Connectionist Models,' IEEE Trans. Software Eng., Vol. 18, pp.563-574, 1992 https://doi.org/10.1109/32.148475
  8. N. Karunanithi, D. Whitley and Y. K. Malaiya, 'Using Neural Networks in Reliability Prediction,' IEEE Software, Vol.18, pp.53-59, 1992 https://doi.org/10.1109/52.143107
  9. R. Lippman, 'An Introduction to Computing with Neural Nets,' IEEE Acoustics, Speech and Signal Processing, pp.4-22, 1987
  10. Y. K. Malaiya, N. Karunanithi, and P. Verma, 'Predictability Measures for Software Reliability Models,' IEEE Trans. on Reliability, Vol.41 , No.4, pp.539-546, 1992 https://doi.org/10.1109/24.249581
  11. K. Matsumoto, T. Inoue, T. Kikuno, and K. Torii, 'Experimental Evaluation of Software Reliability Growth Models,' Proc. IEEE Conf. FTCS-18, pp. 148-153, 1988 https://doi.org/10.1109/FTCS.1988.5313
  12. J. D. Musa, A. Lannino, and K. Okumoto, 'Software Reliability: Measurement, Prediction, Application,' McGraw-Hill, New York, 1987
  13. J. D. Musa, 'Software Reliability Data,' Technical Report, Data and Analysis Center for Software, Rome Air Development Center, Griffins AFB, New York, 1979
  14. M. Ohba, 'Software Reliability Analysis Models,' IBM H. Res. Develop., Vol.28, pp.428-443, 1984
  15. J. Y. Park, S. U. Lee, and J. H. Park, 'Neural Network Modeling for Software Reliability Prediction from Failure Time Data,' Journal of Electrical Eng. and Information Science, Vol.4, No.4, pp.533-538, 1999
  16. C. V. Ramamoorthy and F. B. Bastani, 'Software Reliability-Status and Perspectives,' IEEE Trans. Soft. Eng., SE-8, No.4, July, pp.354-371, 1982
  17. M. L. Shooman, 'Probablistic Models for Software Reliability Prediction,' Statistical Computer Performance Evaluation, New York Academic, pp.485-502, 1972
  18. Y. Thoma, K. Tokunaga, S. Nagase, and Y. Murata, 'Structural Approach to the Estimation of the Number of Residual Software Faults Based on the Hyper-Geometric Distribution,' IEEE Trans. on Software Eng., Vol.15, pp.345-355, 1989 https://doi.org/10.1109/32.21762
  19. Y. Thoma, H. Yamano, M. Ohba, and R. Jacoby, 'Parameter Estimation of the Hyper-Geometric Distribution Model for Real Test/Debug Data,' Dept. Computer Science, Tokyo Inst. Tech., Tech. REP.901002, 1990
  20. B. Widrow and S. D. Sterns, 'Adaptive Signal Processing,' Prantice-Hall, New York, 1985