A non-parametric approach to software reliability prediction

Abstract

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.