In this paper we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.

The non-homogeneous Poisson process (NHPP) has been applied successfully to model nonstationary counting phenomena for a large class of problems. In software reliability engineering, the NHPP-based software reliability models (SRMs) are of a very important class. Since NHPP is characterized by its rate (intensity) function, which is known as the software failure rate of NHPP-based SRM, it is of great interest to estimate accurately the rate function from observed software failure data. In the existing work the same authors introduced a Haar-wavelet-based technique for this problem and found that the Haar wavelet transform provided a very powerful performance in estimating software failure rate. In this paper, we consider the application potentiality of a Daubechies wavelet estimator in the estimation of software failure rate, given the software failure time data. We give practical solutions by overcoming technical difficulties in applying the Daubechies wavelet estimator to the real software failure time data.