The original software reliability demonstration test (SRDT) does not take adequate account of prior knowledge or the prior distribution, which can lead to an expensive use of many resources. In the current paper, we propose a new improved Bayesian based SRDT method. We begin by constructing a framework for the SRDT scheme, then we use decreasing functions to construct the prior distribution density functions for both discrete and continuous safety-critical software, and then present schemes for both discrete and continuous Bayesian software demonstration functions (which we call DBSDF and CBSDF, respectively). We have carried out a set of experiments comparing our new schemes with the classic demonstration testing scheme on several published data sets. The results reveal that the DBSDF and CBSDF schemes are both more efficient and more applicable, and this is especially the case for safety-critical software with high reliability requirements.

Test coverage is a good indicator for testing completeness and effectiveness. This paper utilizes the logistic function to describe the test coverage growth behavior. Based on the logistic test coverage function, a model that relates test coverage to fault detection is presented and fitted to one actual data set. The experimental results show that, compared with three existing models, the evaluation performance of this new model is the best at least with the experimental data. Finally, the logistic test coverage function is applied to the NHPP software reliability modeling for further research.