Parameter Estimation and Prediction Methods for Hyper-Geometric Distribution Software Reliability Growth Model


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 5, No. 9, pp. 2345-2352, Sep. 1998
10.3745/KIPSTE.1998.5.9.2345,   PDF Download:

Abstract

The hyper-geometric distribution software reliability growth model was recently developed and successfully applied. Due to mathematical difficulty of the maximum likelihood method, the least squares method has been suggested for parameter estimation by the previous studies. We first summarize and compare the minimization criteria adopted by the previous studies. It is then shown that the weighted least squares method is more appropriate because of the nonhomogeneous variability of the number of newly detected faults. The adequacy of the weighted least squares method is illustrated by two numerical examples. Finally, we propose a new method for predicting the number of faults newly discovered by next test instances. The new prediction method can be used for determining the time to stop testing.


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Cite this article
[IEEE Style]
P. J. Yang, Y. C. Yeul, L. B. Kwon, "Parameter Estimation and Prediction Methods for Hyper-Geometric Distribution Software Reliability Growth Model," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 5, no. 9, pp. 2345-2352, 1998. DOI: 10.3745/KIPSTE.1998.5.9.2345.

[ACM Style]
Park Joong Yang, Yoo Chang Yeul, and Lee Bu Kwon. 1998. Parameter Estimation and Prediction Methods for Hyper-Geometric Distribution Software Reliability Growth Model. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 5, 9, (1998), 2345-2352. DOI: 10.3745/KIPSTE.1998.5.9.2345.