This paper addresses a family of probability models for the failure time process known as order statistics models. Conventional order statistics models make rather strong distributional assumptions about the detection times: typically they assume that these come from some parametric family of distributions. In this paper a new model is presented that relaxes these distributional assumptions, and—in the tradition of non-parametric statistics generally—‘allows the data to speak for themselves’. The accuracy of the new model is compared on some real data sets with the predictions that come from several of the better parametric reliability growth models © 1998 John Wiley & Sons, Ltd.

The large amount of literature on software reliability assessment and prediction is essentially concerned with parametric models: the inter failure time random variables are assumed to come from parametric families of distributions. Such models involve quite strong assumptions. The motivation for the present work is to relax these assumptions and-in the tradition of non parametric statistics generally-'allow the data to speak for themselves'. We present a new non-parametric model for reliability prediction which is based upon the use of kernel density estimators and compare its accuracy on some real data sets with the predictions that come from several of the better conventional models. These initial results are encouraging: the new models seem to perform as well as the best of the earlier models.

Different software reliability models can produce very different answers when called on to predict future reliability in a reliability growth context. Users need to know which, if any, of the competing predictions are trustworthy. Some techniques are presented which form the basis of a partial solution to this problem. Rather than attempting to decide which model is generally best, the approach adopted allows a user to decide on the most appropriate model for each application.