SOFTWARE-RELIABILITY GROWTH SUPERMODELS

The aim of this paper is to present a method for obtaining a more accu rate 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 fa ctors will depend on the values of the prequential likelihood function s as calculated for each SRGM, the values varying each time a new erro r is observed. The basic models chosen are the Jelinski-Moranda, Goel- Okumoto, Duane, Littlewood-Verrall and Keiller-Littlewood models. Fina lly, we shall compare the SRGMs with the supermodels by using the medi an estimate and deciding if there are any benefits or constraints in a pplying this technique.