故障检测率不规则变化的软件可靠性模型

传统的NHPP(non-homogeneous Poisson process)模型在实际的测试当中被证明是成功的.但是,由于传统的NHPP模型用的是理想的假设,例如,假设故障检测率是常数、平稳变化和规律变化,模型的性能在实际的测试环境中总是受到损害.因此,提出一个基于NHPP的软件可靠增长模型,并且考虑故障检测率的不规则变化情况,这种变化符合故障检测率在实际的软件测试过程中的变化.通过相关的实验验证了所提出的NHPP模型的拟合和预测能力.实验结果表明:在用实际的故障数据进行拟合和预测的过程中,所提出的模型与传统的NHPP模型相比,有更好的拟合和预测性能.同时,也给出了所提出模型相应的置信区间.

Software Reliability Model with Irregular Changes of Fault Detection Rate

The traditional NHPP (non-homogeneous Poisson process) models are proved to be a success in a practical test. However, the model performance always suffers in the realistic software testing environment due to the ideal assumption which derived the traditional NHPP models, such as constant fault detection rate and smooth or regular changes. In this paper, an NHPP-based software reliability growth model is proposed considering an irregular fluctuation of a fault detection rate, which is more in line with the actual software testing process. The fitting and predictive power of the proposed model is validated using the related experiments. The experimental results show the proposed model has a better fitting and predicting performance than the traditional NHPP-based models using the real-world fault data. Meanwhile, the confidence interval is given for the confidence analyses of the proposed model.