Statistical Inference for General-Order-Statistics and Nonhomogeneous-Poisson-Process Software Reliability Models
Statistical Inference for General-Order-Statistics and Nonhomogeneous-Poisson-Process Software Reliability Models
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通阶统计和非齐次泊松过程软件可靠性模型的统计推断
DOI：
10.1109/32.41340
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发表时间：
1989
期刊：
影响因子：
--
通讯作者：
H. Joe
中科院分区：
文献类型：
--
作者：
H. Joe
有许多软件可靠性模型是基于软件调试中错误发生的次数。结果表明，它是可能的软件可靠性模型的顺序统计量或非齐次泊松过程的基础上进行渐近似然推断，渐近置信水平的区间估计参数。特别是，从这些模型的区间估计获得的软件的条件故障率，给定的数据从调试过程中。数据可以分组或取消分组。对于决定何时销售软件的人来说，条件失败率是一个重要的参数。使用区间估计证明了两个数据集，已出现在文献中。>
There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on order statistics or nonhomogeneous Poisson processes, with asymptotic confidence levels for interval estimates of parameters. In particular, interval estimates from these models are obtained for the conditional failure rate of the software, given the data from the debugging process. The data can be grouped or ungrouped. For someone making a decision about when to market software, the conditional failure rate is an important parameter. The use of interval estimates is demonstrated for two data sets that have appeared in the literature. >