Reliability estimation with Weibull inter failure times

In this paper we present three models for the behavior of software failures. By applying these models an attempt has been made to predict reliability growth by predicting failure rates and mean time to next failure of software with Weibull inter failure times at different stages. The changes in the performance of the software as a result of the error removal are described as a Bayes empirical-Bayes prediction in Model I. Model II considers a fully Bayesian analysis with non informative priority of Weibull parameters. An approximation due to Lindley is used in this model as the expressions do not appear in close forms. The M.L. approach is used in Model III. Finally we apply these three models to actual failure data and compare their predictive performances. The comparison of the proposed models is also made in terms of the ratio of likelihoods of observed values based on their predictive distributions.

Among these three models, Model I seems to be quite reasonable as it shows higher reliability growth in all stages. It is noted that this model may be useful to measure the current reliability at any particular stage of the testing process and viewed as a measure of software quality.