Hyper-Geometric Distribution Software Reliability Growth Model : Generalizatio, Estimation and Prediction
Jung-Yang Park, Chang-Yeol Yu, Jae-Hong Park · The Transactions of the Korea Information Processing Society · 1999
The hyper-geometric distribution software reliability growth model (HGDM) was recently developed and successfully applied to real data sets. The HGDM considers the sensitivity factor as a parameter to be estimated. In order to reflect the random behavior of the test-and-debug process, this paper generalizes the HGDM by assuming that the sensitivity factor is a binomial random variable. Such a generalization enables us to easily understand the statistical characteristics of the HGDM. It is shown that the least squares method produces the identical results for both the HGDM and the generalized HGDM. Methods for computing the maximum likelihood estimates and predicting the future outcomes are also presented.