基于测试覆盖的嵌入式软件可靠性评估

为了提高软件可靠性的评估和预测精度,提出了一个基于测试覆盖的非齐次泊松过程类软件可靠性增长模型,通过将测试覆盖率信息考虑到软件可靠性模型中去,使模型能够更准确地描述软件的测试过程,并能避免由于测试的不充分性而导致的可靠性评估偏离软件的真实情况.提出了结合变点思想的基于测试覆盖的软件可靠性评估方法,该方法解决了工程实践中经常出现的测试过程中剖面发生变化而导致失效数据不服从同一分布的实际问题.最后,通过实例分析,验证了该方法的准确性和有效性.     

基于EM方法的隐Markov软件可靠性模型

针对单一软件可靠性模型不能准确描述软件失效行为、无法合理准确地评估预测出软件可靠性的问题,将变点分析引入软件可靠性建模,提出了一种基于隐Markov过程的软件可靠性模型。该模型采用隐变量来描述影响软件可靠性的多种因素,通过隐变量的状态变化刻画出软件过程中各种因素的变化情况,构建出隐Markov链软件可靠性模型,并采用EM算法进行求解,通过实例分析来验证其有效性。实验结果表明,隐Markov链软件可靠性模型具有较强的变点检测能力,并能显著提高软件可靠性拟合精度。     

基于多模型变权重组合的软件可靠性仿真研究

研究提高软件可靠性预测精度问题,对软件可靠性研究已成为当前软件工程的一个研究热点,传统的单一软件可靠性模型由于使用的技术及提取的信息有限,软件可靠性预测精度不高。为提高软件可靠性预测精度,在建立多种单一软件可靠性预测模型的基础上,提出一种样本点的多模型变权重组合模型。将多种预测技术有效地聚合在一起,取长补短,在样本数据有限的情况下,不仅改善了样本内学习能力也增强了样本外的泛化能力,提高了综合预测精度。仿真验证模型无论在样本内还是样本外都较优于经过模拟退火算法优化的BP神经网络(SA-BP)及经过遗传算法优化的最小二乘支持向量机(GA-LSVM),说明变权重组合模型是一种精度更高的软件可靠性失效数据预测模型,具有较好的应用推广价值。     

基于隶属度的软件可靠性综合预测方法

将多模型综合的思想和"变点"思想相结合,给出了一种简单有效且具有普适性的软件可靠性预测方法——基于模糊隶属度的软件可靠性多模型综合预测方法。首先阐述了多模型综合预测问题的一般描述,并介绍了模型评价准则,然后给出了单个模型权重的确定方法和该方法的一般步骤,最后用实际数据验证了方法的有效性及普适性。     

独立序列均值与方差变点的累积和估计及应用

产品生产加工过程中, 反映产品质量参数的均值和方差都有可能出现异常波动, 及时发现质量异常波动, 对于控制产品质量十分重要. 在工业生产、通信工程等领域中独立序列有广泛应用背景. 本文研究了独立序列中均值和方差都存在变点且变点时刻不相同时的变点估计问题, 给出变点时刻的累积和(CUSUM)型估计, 并得到变点估计的收敛速度. 最后将该方法应用于航空发动机管路生产质量控制中和放射医学研究的扫描仪质量控制问题中,模拟结果和实例分析都说明方法的有效性.     

组合模型在软件可靠性预测中的建模与仿真

针对软件可靠性受到多种不确定因素影响,且因素间具有多重共线性,单-预测模型无法全面准确描述其变化规律,导致软件可靠性预测精度不高.为了提高软件可靠性预测的精度,提出一种基于熵值法的软件可靠性组合预测模型.首先采用主成分分析消除软件可靠性度量属性间多重共线性,加快学习速度,然后分别采用AR模型和RBF神经网络对软件可靠性进行预测,采用嫡值法确定两种模型的权重,从而得到组合预测模型的软件可靠性预测值.用NASA的软件度量数据进行模型预测,结果表明,仿真预测模型明显提高了软件可靠性预测精度,说明组合预测方法对软件可靠性预测是可行的.     

变点在软件可靠性分析中的应用论证

本文讨论了软件可靠性的变点分析理论,结合Schneidewind模型提出了软件可靠性变点分析的极大似然方法,并将其应用于实际的软件失效数据集,采用对数PLR图和U-图准则进行检验,结果证明了变点分析方法在软件可靠性分析中的有效性和统计意义。     

基于测试用例和时间域软件可靠性模型

可靠性作为衡量软件质量的重要特性,其定量评估和预测已成为人们关注和研究的焦点。软件可靠性模型既是软件可靠性定量分析的基础,也是可靠性预测的核心和关键。在软件比重日益增加的今天,研究系统的软件可靠性对整个产品质量的提升具有很大的现实意义。现有的软件可靠性模型大都是基于概率统计建立的,考虑的因素比较单一,与工程实际有一定差别。文中对典型的软件可靠性模型进行比较研究,在综合考虑了输入域、缺陷等级、时间域等因素的基础上,经过严密的数学推导,建立了基于测试用例和时间域的软件可靠性混合模型,并对该模型的实际应用进行了介绍。     

软件可靠性数据预处理研究

软件可靠性模型是根据与软件可靠性相关的数据,以统计方法或模糊方法对软件的可靠性进行度量、评估和预测。以往对软件可靠性的预测是针对原始数据进行建模,而原始数据所存在的不平稳性的缺陷,直接导致可靠性模型预测结果的误差较大。本文以软件可靠性模型研究中原始数据存在的问题为出发点,探讨提高软件可靠性预测的方法。通过对可靠性数据的预处理,解决其不平稳性导致预测结果误差较大的问题。     

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.     

一种加入单改变点的Schneidewind模型研究

Schneidewind 模型已经被广泛研究和应用到很多软件可靠性预测中去。很多软件可靠性增长模型都假设软件所有的失效有相同的查错率,并且在失效发生时,查错率也不发生变化。但实际中,查错率会依赖于多种因素,也会因为软件需求的变化、测试团队的变动而发生变化。本文提出通过几何图形的观测通过对 Schneidewind 模型加入单个改变点来改进模型,并通过实验证明此方法对可靠性精度的提高有一定作用。同时,本文也说明了此方法应用的优点及其局限性。     

Full and conditional likelihood approaches for hazard change-point estimation with truncated and censored data

Hazard function plays an important role in reliability and survival analysis. In some real life applications, abrupt changes in the hazard function may be observed and it is of interest to detect the location and the size of the change. Hazard models with a change-point are considered when the observations are subject to random left truncation and right censoring. For a piecewise constant hazard function with a single change-point, two estimation methods based on the maximum likelihood ideas are considered. The first method assumes parametric families of distributions for the censoring and truncation variables, whereas the second one is based on conditional likelihood approaches. A simulation study is carried out to illustrate the performances of the proposed estimators. The results indicate that the fully parametric method performs better especially for estimating the size of the change, however the difference between the two methods vanish as the sample size increases. It is also observed that the full likelihood approach is not robust to model misspecification.     

Multiple change-point detection with a genetic algorithm

 A common change-point problem is considered where the population mean of a random variable is suspected of undergoing abrupt changes in course of a time series. It is usual in practice that no information on positions or number of such shifts is available beforehand. Finding the change points, i.e. the positions of the shifts, in such a situation is a delicate statistical problem since any considered sample may actually represent a mixture of two or more populations where values from both sides of a yet unrecognized change point are unconsciously assembled. If this is the case, underlying assumptions of an employed statistical two-sample test are usually violated. Consequently, no definite decision should be based on just one value of the test statistic. Such a value is rather, as a precaution, to be regarded as an only approximate indicator of the quality of a hypothesis about change-point positions. Given these conclusions, it is found imperative to treat the problem of multiple change-point detection as one of global optimization. A cost function is constructed in such a manner that the change-point configuration yielding the global optimum is compliant with statistical-theoretical requirements to the utmost extent. The used advanced optimization tool, a genetic algorithm, is both efficient – as it takes advantage of the information about promising change-point positions encountered in previously investigated trial configurations – and flexible (as it is open to any modification of the change-point configuration at any time). Experiments using numerical simulation confirm adequate performance of the method in an application where a common change-point detection procedure based on Student's two-sample t-test is used to detect an arbitrary number of shifts in the mean of a normally distributed random variable.     

基于PSOABC-SVM的软件可靠性预测模型

软件可靠性预测是指在软件开发初期对软件中各模块出错的可能性进行预测,对提高软件的可信性具有重要意义。提出了一种基于粒子群与人工蜂群优化支持向量机的软件可靠性预测模型,将粒子群优化算法与人工蜂群算法相结合的混合算法引入到支持向量机的参数选择中,提高软件可靠性预测的效果。实验结果表明,该模型比BP网络预测模型、粒子群优化支持向量机等预测模型收敛速度更快、预测精度更高,能更好的进行软件可靠性预测。     

Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction

Traditional parametric software reliability growth models (SRGMs) are based on some assumptions or distributions and none such single model can produce accurate prediction results in all circumstances. Non-parametric models like the artificial neural network (ANN) based models can predict software reliability based on only fault history data without any assumptions. In this paper, initially we propose a robust feedforward neural network (FFNN) based dynamic weighted combination model (PFFNNDWCM) for software reliability prediction. Four well-known traditional SRGMs are combined based on the dynamically evaluated weights determined by the learning algorithm of the proposed FFNN. Based on this proposed FFNN architecture, we also propose a robust recurrent neural network (RNN) based dynamic weighted combination model (PRNNDWCM) to predict the software reliability more justifiably. A real-coded genetic algorithm (GA) is proposed to train the ANNs. Predictability of the proposed models are compared with the existing ANN based software reliability models through three real software failure data sets. We also compare the performances of the proposed models with the models that can be developed by combining three or two of the four SRGMs. Comparative studies demonstrate that the PFFNNDWCM and PRNNDWCM present fairly accurate fitting and predictive capability than the other existing ANN based models. Numerical and graphical explanations show that PRNNDWCM is promising for software reliability prediction since its fitting and prediction error is much less relative to the PFFNNDWCM.     

一种基于输入域的非参数软件可靠性评估模型

通过分析输入域软件可靠性模型和时间域软件可靠性模型的特点,建立一种基于输入域的非参数软件可靠性评估模型,从而克服一般输入域模型评估精度较差、无法预测的缺点.同时提出了基于非参数统计的方法来估计缺陷数和软件失效概率,从而为利用普通软件测试所获得测试数据进行软件可靠性评估提供了一种解决途径,实例验证表明了该评估模型可以较好地对软件可靠性进行评估,给出缺陷数和软件可靠性的合理估计,其估计精度不低于较好的时间域模型.     

基于层次和数据流驱动的软件可靠性分配方法

为了提高软件可靠性分配的有效性, 提出了一种基于层次和数据流驱动的软件可靠性分配方法。该方法对传统的重要度、复杂度度量方法进行改进; 针对软件系统开发初期体系结构中系统模块层次关系及模块间数据流关系进行抽象, 形成体系结构形式化定义, 建立可靠性因子的度量准则及度量模型, 依据度量模型对可靠性进行分配。最后结合实例进行了分析和验证, 结果表明了该分配模型的有效性和可行性。