Enhancing the predictability of two popular software reliability growth models

Abstract

In this paper, enhancement of the performance of both the Musa-Okumoto and the Goel-Okumoto Software Reliability Growth models are investigated using various smoothing techniques. The method of parameter estimation for the models is the Maximum Likelihood Method. The evaluation of the performance of the models is judged by the relative error of the predicted number of failures over future time intervals relative to the number of failures eventually observed during the interval. The use of data analysis procedures utilizing the Laplace Trend test are investigated. These methods test for reliability growth throughout the data and establish "windows" that censor early failure data and provide better model fits. The research showed conclusively that the data analysis procedures resulted in improvement in both models predictive performance for 41 different sets of software failure data collected from software development labs in the United States and Europe.