Confidence interval estimation of NHPP-based software reliability models

The software reliability growth models (such as NHPP models) are frequently used in software reliability prediction. Estimation of parameters in these models is often done by point estimation. However, some numerical problems arise and make the actual computation hard, especially for automated reliability prediction tools. Here confidence interval computation in Goel-Okumoto model and S-shaped model is studied. The upper and the lower bounds of the parameters can be obtained. For reliability prediction, we implement a simplified Bayesian approach, which delivers improved results. The bounds on the predicted reliability are also computed. Furthermore, numerical problems encountered in earlier point estimation methods are removed by this approach. Our results thus can be used as an important part of the assessment of software quality.