A Logistic Fault-Dependent Detection Software Reliability Model
A Logistic Fault-Dependent Detection Software Reliability Model
复制标题
逻辑故障相关检测软件可靠性模型
DOI：
10.3217/jucs-024-12-1717
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
H. Pham
中科院分区：
文献类型：
--
作者：
H. Pham
在本文中，我们提出了一种逻辑故障相关检测模型，其中软件中检测到的故障的相关率从一开始就增长得更快，但随着测试的进行而增长缓慢，直到达到软件中的最大故障数。导出了所提出模型的在时间 t 时检测到的软件故障的预期数量的显式函数，称为平均值函数。基于归一化秩欧几里德距离 (RED) 和其他标准讨论了模型分析，以说明所提出模型的拟合优度标准，并使用一组软件故障数据将其与几个现有的 NHPP 模型进行比较。还给出了所提出模型的参数估计的置信区间。还讨论了基于美国 7 级或以上地震的真实数据集的数值分析，以说明所提出的模型和最近的逻辑增长模型的拟合优度。结果表明，所提出的模型的拟合效果明显优于所有现有的软件可靠性增长模型。
In this paper, we present a logistic fault-dependent detection model where the dependent-rate of detected faults in the software can grow much faster from the beginning but grow slowly as the testing progresses until it reaches the maximum number of faults in the software. The explicit function of the expected number of software failures detected by time t, called mean value function, of the proposed model is derived. Model analysis is discussed based on normalized-rank Euclidean distance (RED) and other criteria to illustrate the goodness-of-fit criteria of proposed model and compare it to several existing NHPP models using a set of software failure data. The confidence interval for the parameter estimates of the proposed model is also presented. A numerical analysis based on a real data set of the 7 or higher magnitude earthquake in the United States to illustrate the goodness-of-fit of the proposed model and a recent logistic growth model is also discussed. The results show that the proposed model fit significantly better than all the existing software reliability growth models.