异构软件体系结构的可靠性模型

现有基于软件体系结构的可靠性模型只适用于符合马尔可夫过程的软件构件转移关系,而当构件间存在由确定性和随机性转移关系组成的异构结构时,仍按照马尔可夫过程对软件体系结构建模,会导致可靠性评估结果不准确。为了解决现有模型不适用于异构结构的问题,在分析构件转移关系基础上,提出了一个新的软件可靠性模型。基于软件UML顺序图,提出将异构软件结构转换为马尔可夫链的算法。通过对一个实例的可靠性评估,验证了新模型能够比现有模型获得更准确的评估结果。     

一种改进的基于架构的软件可靠性模型

基于架构的软件可靠性分析往往把构件的可靠性当作自身固有不变的属性,忽略了在不同的输入剖面下,因构件所处的交互环境不同造成的实际可靠性的变化。改进了一种基于架构的可靠性模型,引入转移目的构件剖面矩阵来建立系统操作剖面和构件可靠性的联系,并给出了改进后的可靠性合成算法。实例分析表明,该模型可以全面捕捉到在不同系统操作剖面下,因构件之间转移概率和构件可靠度这两个参数的变化对整体可靠性产生的影响,提高了系统设计阶段可靠性分析的精确性。     

一种软件可靠性测试模型研究

在马尔可夫链模型的基础上,将测试问题转化为一个数学问题。通过建立软件的使用链,根据使用链进行序列抽样,产生测试用例,将软件测试结果的分析问题转化为一个经典概率问题。运用实例证明,这种技术具有一定的实用性和有效性。     

A Bayesian predictive software reliability model withpseudo-failures

In our previous paper (2000), a Bayesian software reliability model with stochastically decreasing hazard rate was presented. Within any given failure time interval, the hazard rate is a function of both total testing time as well as number of encountered encountered failures. In this paper, to improve the predictive performance of our previously proposed model, a pseudo-failure is inserted whenever there is a period of failure-free execution equals (1-α)th percentile of the predictive distribution for time until the next failure has passed. We apply the enhanced model with pseudo-failures inserted to actual software failure data and show it gives better results under the sum of square errors criteria compared to previous Bayesian models and other existing times between failures models     

An extended domain-based model of software reliability

A definition of software reliability is proposed in which reliability is treated as a generalization of the probability of correctness of the software in question. A tolerance function is introduced as a method of characterizing an acceptable level of correctness. This in turn is used, together with the probability function defining the operational input distribution, as a parameter of the definition of reliability. It is shown that the definition can be used to provide many natural models of reliability by varying the tolerance function and that it may be reasonably approximated using well-chosen test sets. It is also shown that there is an inherent limitation to the measurement of reliability using finite test sets     

A non-parametric order statistics software reliability model

This paper addresses a family of probability models for the failure time process known as order statistics models. Conventional order statistics models make rather strong distributional assumptions about the detection times: typically they assume that these come from some parametric family of distributions. In this paper a new model is presented that relaxes these distributional assumptions, and—in the tradition of non-parametric statistics generally—‘allows the data to speak for themselves’. The accuracy of the new model is compared on some real data sets with the predictions that come from several of the better parametric reliability growth models © 1998 John Wiley & Sons, Ltd.     

Analysis of object-oriented software reliability model development

Can object-oriented methods be applied to mathematical software? Apparently, according to Beall and Shepard (An object-oriented framework for reliable numerical simulations, object-oriented software. Addison Wesley, Reading, 1994) who say: “It has been recognized in a number of different fields that object-oriented programming, in general, and software frameworks, in particular, provide a means to allow the efficient construction and maintenance of large scale software systems. Since general purpose numerical analysis codes certainly qualify as large-scale software it makes sense for us to see how these methodologies can be applied to this field.”     

一类软件可靠度新模型的统计推断

对软件可靠性的著名模型——JM模型进行了修改和研究,给出了模型中的参数的极大似然估计存在的充要条件。     

软件可靠性增长G-O模型的改进

在传统的软件可靠性增长G-O模型中,故障检测率和初始的故障总数是影响软件可靠性的2个重要因素.为了提高软件可靠性评估的可信性,考虑到在软件纠错的过程中可能会引入新的错误,把模型中潜在的故障总数和故障检测率看作随时间变化的函数,提出了改进的G-O模型,给出了解析方法,并将改进前后的G-O模型进行了对比,通过实例进行了验证...     

一种考虑软件补丁的软件可靠性模型研究

将调整因子引入到关于软件产品的可靠性预计模型中,提出了改进的关于软件产品的软件可靠性预计模型.通过一组数据仿真证明,改进模型比原模型有着更好的性能,更加符合现实的软件可靠性活动过程.     

A time/structure based software reliability model

The past 20 years have seen the formulation of numerous analytical software reliability models for estimating the reliability growth of a software product. The predictions obtained by applying these models tend to be optimistic due to the inaccuracies in the operational profile, and saturation effect of testing. Incorporating knowledge gained about some structural attribute of the code, such as test coverage, into the time-domain models can help alleviate this optimistic trend. In this paper we present an enhanced non-homogeneous Poisson process (ENHPP) model which incorporates explicitly the time-varying test-coverage function in its analytical formulation, and provides for defective fault detection and test coverage during the testing and operational phases. It also allows for a time varying fault detection rate. The ENHPP model offers a unifying framework for all the previously reported finite failure NHPP models via test coverage. We also propose the log-logistic coverage function which can capture an increasing/decreasing failure detection rate per fault, which cannot be accounted for by the previously reported finite failure NHPP models. We present a methodology based on the ENHPP model for reliability prediction earlier in the testing phase. Expressions for predictions in the operational phase of the software, software availability, and optimal software release times subject to various constraints such as cost, reliability, and availability are developed based on the ENHPP model. We also validate the ENHPP model based on four different coverage functions using five failure data sets. This revised version was published online in June 2006 with corrections to the Cover Date.     

基于神经网络的级联软件可靠性模型

针对一般经典软件可靠性模型适用范围的局限性问题和预测精度问题,提出了一种新的级联模型.将4个经典软件可靠性模型的输出作为误差背向传播(error back propagation,BP)神经网络的输入,级联组合成一个软件可靠性模型,称之为级联软件可靠性模型.通过对一组经典的实际软件故障数据SYS1进行实验,将级联软件可靠性模型与4个经典软件可靠性模型预测的结果进行对比,结果表明级联软件可靠性模型的预测精度要远远高于4个经典软件可靠性模型,而且具有更好的通用性.     

软件可靠性组合预测模型研究

根据灰色模型、谐波分析和时间序列分析理论,对软件测试阶段的失效数据构成的时间序列进行分析,得到软件可靠性组合预测模型。结合实际数据,给出了具体的实现方法。数据试验结果表明,与单一时间序列预测模型相比,该模型具有较高的预测精度和很好的模型适应性。     

基于云模型的可信软件可靠性度量模型

针对可信软件可靠性定性评估问题,将云模型理论与GB/T16260中软件可靠性度量指标相结合,提出了一种基于云模型的软件可靠性度量模型.模型根据可靠性度量指标提取样本信息,通过云发生器生成云模型,运用合并云算法将多个属性云合并为一个综合云模型,使用带权重的欧氏距离云决策算法确定综合云模型的可靠性等级.通过某安全关键软件验证,该模型能够更加客观、真实地反映软件的可靠性.     

一种基于离散时间的NHPP软件可靠性增长模型

基于非齐次泊松过程的软件可靠性增长模型按时间域可分为连续时间模型和离散时间模型两类。现有的软件可靠性增长模型大多都是针对连续时间构造的,在一定程度上忽视了对离散时间模型的研究。利用概率生成函数构建两种基于离散时间的软件可靠性增长模型——基本模型和扩展模型,具有很大的实用性和必要性。构建的扩展模型以不完美排错情形作为基础,考虑到了由于故障排除而有可能引入新故障的问题,同时还考虑到了在软件排错过程中由于测试团队的熟练程度而引起的软件故障排除率的相对变化情况,这使得提出的模型更加符合实际。最后利用两组公开发表的     

基于软件过程的软件可靠性预测运作模型

提出了基于软件开发过程数据,构造用于可靠性预测的过程数据库,并分析了此数据库的内容与运作模型,以期得到更加可信、准确的软件可靠性预测结果,为后续的研究奠定数据基础.     

软件可靠性预测的ARIMA方法研究

对基于求和自回归滑动平均模型（ARIMA模型）的软件可靠性预测方法进行了研究,提出了将软件可靠性失效数据看作时间序列,通过建立相应的ARIMA（p,d,q）模型来进行预测的方法。对该方法的基本思想、模型表述、建模流程进行了详细介绍,并依据上述方法选用Musa经典数据集中的Project SS2中的数据进行了预测,结果表明预测的准确性较高,说明该方法适用于软件可靠性预测。     

基于Kohonen网络的软件可靠性模型选择

软件可靠性模型是软件可靠性工程的一个重要方面。现在还没有一个通用的模型,模型选择问题已成为模型研究的重点。运用聚类思想对软件可靠性模型的选择进行研究。在对软件失效数据进行编码的基础上,采用Kohonen神经网络对其进行聚类分析,从而实现了可靠性模型的选择。最后通过仿真测试,证明了此方法的有效性和可行性。     

基于多准则决策的软件可靠性模型选择方法

针对现有软件可靠性模型选择方法计算复杂和适用性差的问题,提出基于多准则决策的软件可靠性模型选择方法.定义了指导软件可靠性模型选择的若干准则,包括生命周期阶段准则、模型输入要求准则、模型输出要求准则、模型假设吻合准则和失效数据趋势准则,将这些准则分为确定性准则和不确定性准则,阐述了基于这些准则进行软件可靠性模型选择的算法,并实例验证了该方法的简单可行性.指出将这种方法与现有软件可靠性模型选择方法进行综合运用的研究方向.     

基于未确知聚类的软件可靠性模型研究

软件可靠性是软件质量的重要指标之一。传统的软件可靠性模型通过一些精确、无歧义的假设,对软件的可靠性增长行为进行抽象和简化。将未确知理论与聚类方法结合起来应用于软件可靠性的研究中,摆脱了对软件故障过程的各种分布假设,改善了模型应用中的不一致问题。最后通过实例对新算法和传统算法进行了比较与分析。