Confidence interval estimation of NHPP-based software reliability models

Software reliability growth models, such as the non-homogeneous Poisson process (NHPP) models, are frequently used in software reliability prediction. The estimation of parameters in these models is often done by point estimation. However, some numerical problems arise with this approach, and make the actual computation hard, especially for automated reliability prediction tools. In this paper, confidence interval computation is studied in the Goel-Okumoto (1979) model and the S-shaped model (S. Yamada et al., 1983). 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, the numerical problems encountered in earlier point estimation methods are removed by this approach. Our results can thus be used as an important part of the assessment of software quality.