Predicting software reliability via completely monotone nonparametric estimator with grouped data

Nonparametric software reliability analysis is a challenging issue to predict software reliability under incomplete knowledge on software fault-detection time distribution, because the underlying stochastic model is a function of only software fault data and is not predictable in principle for unknown patterns in future long term. Sofer and Miller (1991) develop a unique approach based on a completely monotone nonparametric estimator, but just focus on the fault-detection time data. However, such data sets are seldom available in practice, and their approach is not applicable to many actual software development processes. In this paper, we revisit the seminal completely monotone estimator by Sofer and Miller (1991) to use in the major case with grouped data, and develop both estimation and prediction methods of software fault count. We investigate the potential performance of our distribution-free method with thirteen actual software development project data, and compare it with the existing parametric models known as nonhomogeneous Poisson process-based software reliability models.