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Software reliability estimation of gamma failure time models

机译:伽马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:随着软件在各个领域中的作用越来越大,人们对软件产品质量的关注也越来越大。一种这样的软件质量度量是可靠性,它是计算机程序在指定环境中指定时间无故障运行的概率。在发布软件之前,会在测试过程中获取故障数据,使用该数据可以评估软件的未来可靠性。可以使用各种方法(例如平均失效时间,失效强度函数,平均值函数等)来进行可靠性评估。要评估可靠性,应使用一种数学模型来描述失效随时间的行为。这种模型称为软件可靠性模型。基于故障时间分布,已经定义了几类软件可靠性模型。一种此类模型是伽马故障时间模型,其中假定故障时间遵循伽马分布。本文利用最大似然估计和最小方差无偏估计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,可以估计将来某个时间点的软件可靠性。已经考虑通过案例研究来比较这两个估计值。

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