Enhancing the predictive performance of the Goel-Okumoto software reliability growth model

Abstract

In this paper, enhancement of the performance of the Goel-Okumoto Reliability Growth model is investigated using various smoothing techniques. The method of parameter estimation for the model is the maximum likelihood method. The evaluation of the performance of the model 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 the models' predictive performance for 41 different sets of software failure data collected from software development labs in the United States and Europe.