Recalibrating Software Reliability Models

In spite of much research effort, there is no universally applicable software reliability growth model which can be trusted to give accurate predictions of reliability in all circumstances. Worse, we are not even in a position to be able to decide a priori which of the many models is most suitable in a particular context. Our own recent work has tried to resolve this problem by developing techniques whereby, for each program, the accuracy of various models can be analyzed. A user is thus enabled to select that model which is giving the most accurate reliability predictions for the particular program under examination. One of these ways of analyzing predictive accuracy, which we call the u-plot, in fact allows a user to estimate the relationship between the predicted reliability and the true reliability. In this paper we show how this can be used to improve reliability predictions in a very general way by a process of recalibration. Simulation results show that the technique gives improved reliability predictions in a large proportion of cases. However, a user does not need to trust the efficacy of recalibration, since the new reliability estimates produced by the techn ique are truly predictive and so their accuracy in a particular application can be judged using the earlier methods. The generality of this approach would therefore suggest that it be appl ied as a matter of course whenever a software reliability model is used. Indeed, al though this work arose from the need to address the poor performance of soffware reliability models, it is likely to have applicability in other areas such as reliability growth model ing for hardware.