Software reliability growth supermodels
The aim of this paper is to present a method for obtaining a more accurate prediction for software reliability growth models (SRGMs). It is our belief that if we try to use a more general approach implying the building of a supermodel as a weighted sum of several SRGMs, it may be possible to obtain more accurate results in prediction. The weight factors will depend on the values of the prequential likelihood functions as calculated for each SRGM, the values varying each time a new error is observed. The basic models chosen are the Jelinski-Moranda, Goel-Okumoto, Duane, Littlewood-Verrall and Keiller-Littlewood models. Finally, we shall compare the SRGMs with the supermodels by using the median estimate and deciding if there are any benefits or constraints in applying this technique.