- Volume 23 Issue 4
DOI QR Code
Two model comparisons of software reliability analysis for Burr type XII distribution
- An, Jeong-Hyang (Department of Internet Information, Daegu Haany University)
- Received : 2012.06.14
- Accepted : 2012.07.14
- Published : 2012.07.31
In this paper reliability growth model in which the operating time between successive failure is a continuous random variable is proposed. This model is for Burr type XII distribution with two parameters which is discussed in two versions: the order statistics and non-homogeneous Poisson process. The two software reliability measures are obtained. The performance for two versions of the suggested model is tested on real data set by U-plot and Y-plot using Kolmogorov distance.
- Abdel-Ghaly, A. A. (1996). A new model for software and hardware faults's removel process. Economic and Business Review, Faculty of Commerce, Ain-shams University, 1, 23-47 .
- Abdel-Ghaly, A. A., Al-Dayian, G. R. and Al-Kashkari, F. H. (1997). The use of Burr type XII distribution on software modelling. Microelectro Reliability, 37, 305-313. https://doi.org/10.1016/0026-2714(95)00124-7
- Abdel-Ghaly, A. A. and Attiah, A. F. (1990). A Bayesian analysis of theWeibull model of software reliability. Proceedings of 25th Annual Conference Institute of Statistical Studies and Research, 25, 15-34.
- Abdel-Ghaly, A. A., Chan, P. Y. and Littlewood, B. (1986). Evaluation of computing software reliability predictions. IEEE Transaction Software Engineering, 12, 950-967. https://doi.org/10.1109/TSE.1986.6313050
- Brocklehurst, S. and Littlewood, B. (1992). New ways to get accurate reliability measures. IEEE Software, 9, 34-42. https://doi.org/10.1109/52.143100
- Goel, A. L. (1980). Software error detecting model with applications. Journal of System Software, 1, 243- 249.
- Goal, A. L., and Okumoto, K. (1979). Time-dependent error-detection rate model for software reliability and other performance measure. IEEE Transactions on Reliability, 28, 206-211. https://doi.org/10.1109/TR.1979.5220566
- Jelinski, K. and Moranda, P. (1972). Software reliability research. In Statistical Computer Performance Evaluation, edited by W. Freiberger, Academic Press, London, 465-484.
- Karanta, I. (2006). Methods and problems of software reliability estimation, VTT working papers 63, VTT Technical Research Center of Finland.
- Lawless, J. F. (2003). Statistical models and methods for lifetime data, 2nd edition, John Wily and Sons, New York.
- Lee, J. and Lee, C. (2010). Reliability and ratio in aright truncated Rayleigh distribution. Journal of the Korean Data & Information Science Society, 21, 195-200.
- Lee, J. and Yoon, S. C. (2008). Software reliability for order statistic of Burr XII Distribution. Journal of the Korean Data & Information Science Society, 19, 1361-1369.
- Littlewood, B. (1981). Stochastic reliability growth: A model for fault removal in computer programs and hardware design. IEEE Transactions on Reliability, 30, 13-320.
- Miller, D. R. (1984). Exponential order statistics models of software reliability growth, Technical Report T-496184, George Washington University, Washington, U.S.A..
- Moon, Y. and Lee, C. (2011). Estimating reliability in discrete distributions. Journal of the Korean Data & Information Science Society, 22, 811-817.
- Nalini, R., Zhaohui, L., and Bonnie, K. R. (2008). NHPP models with Markov switching for software reliability. Computational Statistics and Data Analysis, 52, 3988-3999. https://doi.org/10.1016/j.csda.2008.01.010
- Pham, H. (2000). Software reliability, New York, Berlin.
- Park, J. Y., Park, J. H. and Fujiwara, T. (2006). Frameworks for NHPP software reliability growth models. International Journal of Reliability and Applications, 7, 155-166.
- Rahman, M. and Muraduzzaman, S. M. (2010). Likelihood ratio in estimating gamma distribution param- eters. Journal of the Korean Data & Information Science Society, 21, 345-354.
- Singpurwalla, N. and Wilson, S. (1999). Statistical methods in software engineering : Reliability and risk, Springer, New York.