Parameter estimation of the hyper-geometric distribution model for real test/debug data
Yoshihiro Tohma, Hitoshi Yamano, M. Ohba, R. Jacoby · 2002
The hyper-geometric distribution model (HGDM) has been proposed for estimating the number of faults initially resident in a program at the beginning of the test/debug process. However, the parameters of the hyper-geometric distribution necessary for making the estimation were previously determined by the 3-dimensional exhaustive search and therefore, much time was needed to get the numerical result. The authors demonstrate, using real test/debug data of programs, that the least square sum method can be well applied to the estimation of such parameters of the hyper-geometric distribution model. Thus, the time needed for calculating the estimates can be reduced greatly.>