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Software reliability estimation of gamma failure time models

机译:伽玛故障时间模型的软件可靠性估计

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With the increasing role of software in every field, concern has grown over the quality of software products. One such measure of software quality is the reliability, which is the probability of failure-free operation of a computer program in a specified environment for a specified time. Prior to the release of software, failure data are obtained during testing, using which, future reliability of software can be assessed. Reliability assessment can be done using various measures like Mean Time To Failure, failure intensity function, mean value function, etc. To assess the reliability, one should have a mathematical model that describes the behavior of failure with time. Such models are called software reliability models. Several classes of software reliability models have been defined based on the failure time distribution. One such class of models is the gamma failure time models, where failure times are assumed to follow gamma distribution. In this paper, software reliability estimates of gamma failure time models have been obtained using the method of Maximum Likelihood Estimation and method of Minimum Variance Unbiased Estimation. Using these methods, reliability of the software at a future time point can be estimated. Case studies have been considered to compare the two estimates.
机译:随着软件在每个领域的作用越来越大,关注的是在软件产品的质量上增长。一种这样的软件质量测量是可靠性,这是指定时间在指定环境中的计算机程序的失败操作的概率。在软件发布之前,在测试期间获得故障数据,使用哪种情况,可以评估软件的未来可靠性。可靠性评估可以使用平均故障,故障强度函数,平均值等的各种测量来完成,以评估可靠性,应该有一个数学模型,该模型描述了与时间失败的行为。这些模型称为软件可靠性模型。基于故障时间分布定义了几种类别的软件可靠性模型。其中一个类别的模型是伽玛失效时间模型,假设故障时间遵循伽马分布。本文使用了最大似然估计方法和最小方差估计方法的方法获得了伽马故障时间模型的软件可靠性估计。使用这些方法,可以估计在未来的时间点处的软件的可靠性。案例研究已被认为比较两项估计。

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