An Adaptive Concatenated Failure Rate Model for Software Reliability
提出一种两参数自适应软件可靠性模型，其失效率函数反映工程师对软件故障随机性的认知，并用贝叶斯方法估计参数和检验可靠性增长。
Abstract This article introduces a software reliability model whose concatenated failure rate function is motivated via considerations that reflect an engineer's knowledge about the stochastic nature of software failures. The model is adaptive (in a sense explained), has two parameters, and has characteristics that generalize those of existing models. A Bayesian approach for estimating the model parameters and for testing hypotheses about reliability growth is proposed. The prior distributions reflect structural considerations, and Markov chain Monte Carlo techniques are used to implement the approach.