Browse > Article
http://dx.doi.org/10.3745/KIPSTD.2003.10D.5.829

Performance estimation for Software Reliability Growth Model that Use Plot of Failure Data  

Jung, Hye-Jung (평택대학교 정보통계학과)
Yang, Hae-Sool (호서대학교 벤처전문대학원)
Park, In-Soo (산업자원부 기술표준원)
Publication Information
The KIPS Transactions:PartD / v.10D, no.5, 2003 , pp. 829-836 More about this Journal
Abstract
Software Reliability Growth Model that have been studied variously. But measurement of correct parameter of this model is not easy. Specially, estimation of correct model about failure data must be establish and estimation of parameter can consist exactly. To get correct testing, we calculate the normal score and describe the normal probability plot. Use the normal probability plot, we estimate the distribution for failure data. In this paper, we estimate the software reliability growth model for through the normal probability plot. In this research, we applies software reliability growth model through distribution characteristics of failure data. If we see plot, we determine the software reliability growth model, we can make sure superior in model's performance estimation.
Keywords
Software Reliability Growth Model; Reliability Function; Accuracy; Noise; Plot of Failure Data;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Bertolino & L. Strigini, 'On the use of testability mea-sures for dependence assessment,' IEEE Trans. Soft, Eng., Vol.22, pp.97-108, 1996   DOI   ScienceOn
2 B. Littlewood & A. Sofer, 'A Bayesian Modification to the Jelinski-Moranda software reliability growth model,' IEE/BCS Soft, Eng., Journal, pp.31-41, 1987
3 B. Littlewood & J. L. Verrall, 'A Bayesian reliability model with a stocastically monotone failure rate,' IEEE Trans. Reliablility, pp.108-114, Vol.23, 1974   DOI   ScienceOn
4 A. Csenki, 'Bayes predictive Analysis of a fundamental So-fware Reliability Model,' IEEE Trans. Reliability, Vol.39, No.2, pp.177-183, 1990   DOI   ScienceOn
5 A. L. Goel & K. Okumoto, 'Time Dependent Error Detection Rate Model for Software Reliability and Other Performance Measures,' IEEE Trans. Reliability, Vol.28, pp.206-211, 1979   DOI   ScienceOn
6 B. Littlewood, 'Forecasting software reliability,' In Soft-ware Reliability Modelling and Identification, Ed. S. Bittanti, Springer-verlag, Berlin, pp.141-209, 1988
7 C. N. Morris, 'Parametric empirical Bayes inference : The-ory and application,' J. American Statistical Association, Vol.78, pp.47-65, 1983   DOI
8 L. H. Crow & N. D. Singpurwalla, 'An Empirically Deve-loped Fourier Series Model for Describing Software Fai-lure,' IEEE Trans. Reliability, Vol.33, pp.176-183, 1984   DOI   ScienceOn
9 S. Campodonico & N. D. Singpurwalla, 'A Bayesian Anal-ysis of the Logarithmic Poisson Execution Time Model Based on Expert Opinion and Failure Data,' IEEE Trans. Soft.Eng., Vol.20, No.9, pp.677-683, 1994   DOI   ScienceOn
10 N. Langberg & N. D. Singpurwala, 'A Unification of some Software Reliability Model,' SIGM Journal on Scientific and Statistical Computation, pp.781-790, 1985   DOI
11 N. Davies, 'The Musa data revisited : alternative methods and structure in software reliability modelling and anal-ysis,' In Achieving Safety and Reliability with Computer Systms, Ed. B. K. Daniels, Elsevier, pp.118-130, 1987
12 P. Zeephongsekul & G. Xie. & S. Kumar, 'Software-Reli-ability Growth Model : Primary-Failures Generate Secon-dary-Faults Under Imperfect Debugging,' IEEE Trans. Reliability, Vol.43, pp.408-413, 1994   DOI   ScienceOn
13 Z. Jelinski & P. B. Moranda, 'Software Reliability Research, In Statistical Computer Performance Evaluation,' New York, Academic Press, pp.465-484, 1972