Regression Based Software Reliability Estimation: Duane Model

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-26 Number-1
Year of Publication : 2015
Authors : Dr. R. Satya Prasad, Mr. N. V.K. Stanley Raju
DOI :  10.14445/22312803/IJCTT-V26P108


Dr. R. Satya Prasad, Mr. N. V.K. Stanley Raju "Regression Based Software Reliability Estimation: Duane Model". International Journal of Computer Trends and Technology (IJCTT) V26(1):45-49, August 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Software Reliability Growth Model(SRGM) is a mathematical model which represent, how the software reliability improves as faults are detected and repaired. The performance of SRGM is judged by its ability to fit to the software failure data. How good does a mathematical model fit to the data and reliability of software is presented in the current paper, considering Duane model. Regression method is used to estimate the model parameters. To assess the performance of the considered Software Reliability Growth Model, the parameters are estimated based on the real software failure data sets.

1. Ananda Sen, (1998). “Estimation of Current Reliability in a Duane-Based Reliability Growth Model”, Technometrics, Vol. 40, No. 4, pp. 334-344.
2. Ascher. H and Feingold. H. (1984). Repairable Systems Reliability, Inference, Misconceptions and their Causes. Marcel Dekker, New York.
3. Ashoka. M.,(2010), “Sonata Software Limited” Data Set, Bangalore.
4. Crow. L. H. (1974). “Reliability for Complex Systems, Reliability and Biometry”. Society for Industrial and Applied Mathematics (SIAM), pp.379-410.
5. Duane, J.T. (1964), “Learning curve approach to reliability monitoring”, IEEE Transactions on Aerospace, Vol. 2, pp. 563-6.
6. Ehrlich, W., Prasanna, B., Stampfel, J. and Wu, J. (1993). “Determining the cost of a stop testing decision”, IEEE Software: 33-42.
7. Higgins, J. J. and Tsokos, C.P. (1981), “A Quasi-Bayes Estimate of the failure intensity of a reliability gowth model”, IEEE Transactions on Reliability, R-30, pp471-475.
8. Jelinski, Z. and Moranda, P. (1972). “Software Reliability Research”, In Statistical Computer Performance Evaluation, W. Freiberger, Ed.New York,: Academic, 465-484.
9. Khoshgoftaar, T.M. and Woodstock, T.G. (1991). “Software reliability model selection: a case study”, Proc. Int. Symp. On software reliability engineering, may 18-19, Austin, Texas, pp.[183-191].
10. Lyu, M.R. and Nikora, A. (1991). “A Heuristic approach for software reliability prediction: the equally weighted linear combination model. Proc. Int. Symp. On software reliability engineering, may 18-19, Austin, Texas, pp.[172-181].
11. Lyu, M.R., (1996). “Handbook of Software Reliability Engineering”, McGrawHill, New York.
12. Ohba, M. (1984a). “Software reliability analysis models”, IBM J Research Development, Vol 28(4).
13. Rigdon, S.E., (1989). “Testing goodness-of-fit for the power law process”, communications in statistics: theory and methods, Vol.18, pp.4665-4676.
14. Satya Prasad. R, Srisaila. A and Krishna Mohan. G. (2014), “SPC on Ungrouped Data: Power Law Process Model”, International Journal of Software Engineering. Volume 5, Number 1, pp. 7-16.
15. Srisaila. A, Krishna Mohan. G and Satya Prasad, R. (2015). “SPC and Order Statistics: Plp Model”, International Journal of Applied Engineering Research, Volume: 10, Number: 4 , pp.10985-10994.
16. Suresh. N. (1992). “Modeling and Analysis of Software Reliability”. Ph.D. thesis, University of South Florida, Tampa, FL.
17. Xie. M, T.N Goh and P.Ranjan. (2002). “Some effective control chart procedures for reliability monitoring”, Reliability Engineering and System Safety. 77, 143-150

Duane model, Regression, Goodness-of-fit, AIC, Reliability.