软件可靠性评估的重要抽样方法

基于统计测试的马尔可夫使用模型对软件可靠性评估提出了一种有效的估计方法.该方法利用重要抽样技术在保证可靠性估计无偏性的条件下,利用交叉熵度量操作剖面与零方差抽样分布之间的差异,通过启发式迭代过程调整各个状态之间的转移概率来修正测试剖面.从理论上证明了利用修正测试剖面测试估计的可靠性是方差为0的无偏估计.最后给出了软件可靠性估计的最优测试剖面生成的启发式迭代算法.仿真结果表明,该方法与模拟退火算法相比,能够明显降低估计的方差,在提高估计精度的同时加快统计测试速度.     

Matlab统计工具箱的若干优化补充

针对Matlab 6.5统计工具箱没有优化正态总体方差的区间估计,且没有给出正态总体方差检验的问题,通过编写Matlab程序,优化统计工具箱对正态总体方差的区间估计,开发正态总体方差的假设检验算法.实例表明该方法在推断统计方面实用性较强.     

基于故障剖面的安全关键系统可靠性测试与评估

可靠性测试是安全关键系统可靠性评估的重要手段。论文结合在某电信系统的工程实践,介绍一种基于故障剖面的可靠性测试和评估的方法：通过逆向工程从已有的安全关键系统的失效事件中分析提取出故障概率数据,结合故障注入测试对系统的可靠性进行评估。该方法直接从故障入手,不受缺乏缺陷引发故障概率数据问题的困扰,并通过故障模式的双层模型明确测试范围,简化了评估过程。     

考虑估计方差的软件可靠性估计

软件可靠性是软件质量保证（SQA）工程的关键。本文分析现有的软件组件可靠性估计的参数模型,在考虑组件可靠性估计方差的基础上,提出一种系统可靠性估计的非参数模型。     

FPGA软件操作序列剖面驱动的可靠性测试技术研究

操作剖面及其构造方法在软件可靠性测试中已经被广泛应用,然而,在FPGA软件测试的场景下,由于操作的时序性、操作之间的约束性,以及操作的连续性,使得其传统的操作剖面及构造方法存在不能反映操作之间的约束关系、时序关系和随机性的缺点,难以满足测试用例的合理性和覆盖性。提出一种基于操作序列剖面驱动的可靠性测试方法,根据不同的阶段,采用“六步构造法”构造相互独立的操作序列剖面,同时,以相机控制FPGA软件为例进行了可靠性用例生成并进行了测试可靠度评估。评估结果表明,该方法生成的测试用例效率高、代价低、通用性强,能够对FPGA软件的可靠性进行有效的验证。提出的方法将为FPGA软件可靠性测试提供一种具有较高实用价值的技术手段,以满足FPGA复杂系统软件的越来越高的测试需求。     

统计测试的软件可靠性保障能力研究

研究了统计测试对不同软件的测试能力,提出将测试有效性信息综合到软件可靠性评估模型中,以提高可靠性估计的针对性和精度.通过实验证明了该方法的合理性.该方法为高可靠性的软件质量保障要求提供了途径和可能.     

多传感器测量中的方差估计

在多传感器加权融合算法中，各传感器的权值仅由传感器的测量方差决定。假设各传感器噪声为平稳过程且相互独立，本文提出了待测状态未知且时变情况下．多传感器测量方差的估计算法，并讨论了估计方差的统计特性，证明了估计的无偏性。针对工程中环境噪声的变化，进一步提出了加窗方差估计方法，并给出了窗口宽度和估计精度的关系．使算法能有效地跟踪环境噪声的变化。该算法无需设置依赖于环境的初始值，并给出了递推公式．使其可用于对多传感器测量方差的实时自适应估计。仿真结果直观地说明了估计方法的有效性。     

基于多版本较准的软件可靠性评估

软件可靠性模型都要求测试时的操作剖面与实际运行时的操作剖面一致,但这往往很难达到,造成测试完成之后的可靠性预计与发布之后实际运行中达到的可靠性有较大差距.为了提高软件可靠性评估的准确性,提出了剖面差异性的概念,认为同一软件各个版本的测试操作剖面与实际操作剖面之间的差异性是相同的.在此前提下,提出了一个多版本校准方法,利用软件以前版本的剖面差异性来改进软件当前版本的可靠性评估.     

基于加速剖面的软件防危性验证测试方法

在严格区分软件防危性和可靠性的基础上,提出一种基于加速剖面的软件防危性验证测试方法。该方法通过系统性的防危分析,构建软件加速剖面,根据重要性取样原理求得测试加速因子,能在减少测试代价的同时实现对软件防危性指标的高可信验证测试。     

余度IMU精度分析与数据融合方法改进

针对现有余度IMU（inertial measurement unit）数据融合精度不高的问题,在对六单轴传感器非正交配置的余度IMU数据融合特性及测量误差与融合误差之间关系进行研究的基础上,提出了1种基于最小二乘简单、有效的余度传感器测量误差估计及数据融合方法。首先对最小二乘融合的空间矢量在测量轴上的投影与测量值之间的误差进行方差统计,统计结果作为传感器测量误差估计的依据及加权最小二乘的权值参考。然后利用加权最小二乘对传感器测量数据进行融合。仿真结果表明：该方法能有效估计传感器测量误差大小,提高余度IMU数据融合及余度惯性导航系统精度。     

基于重要抽样的软件统计测试加速

本文提出一种基于重要抽样的软件统计测试加速方法,该方法通过调整软件Markov链使用模型的迁移概率,在根据统计测试结果得到软件可靠性无偏估计的前提下,可以有效提高安全攸关软件的测试效率,部分解决了安全攸关软件统计测试时间和费用开销过大的问题。同时,本文给出了计算优化迁移概率的模拟退火算法。实验仿真结果表明,该方法可以有效地提高安全攸关软件统计测试的效率。     

Software dependability evaluation based on Markov usage models

A general technique for computing optimal state transition probabilities for software tests, based on a Markov usage model, is developed. The optimization criterion is maximum precision of unbiased dependability estimates derived from the test results. Three different dependability measures are considered: (i) risk, (ii) safety, and (iii) reliability. As input, pre-information on failure probabilities and losses in case of failure related with single operations is used. The optimization itself is done by means of a numerical procedure which is fast because of the convexity of the underlying stochastic optimization problem. The procedure can be improved by the construction of a distribution with a common lower bound on state transition probabilities; this distribution may also be used in the more general context of structural statistical testing of software.     

Generating transition probabilities to support model‐based software testing

Markov chain usage models support test planning, test automation, and analysis of test results. In practice, transition probabilities for Markov chain usage models are often specified using a cycle of assigning, verifying, and revising specific values for individual transition probabilities. For large systems, such an approach can be difficult for a variety of reasons. We describe an improved approach that represents transition probabilities by explicitly preserving the information concerning test objectives and the relationships between transition probabilities in a format that is easy to maintain and easy to analyze. Using mathematical programming, transition probabilities are automatically generated to satisfy test management objectives and constraints. A more mathematical treatment of this approach is given in References [ 1 ] (Poore JH, Walton GH, Whittaker JA. A constraint‐based approach to the representation of software usage models. Information and SoftwareTechnology 2000; at press) and [ 2 ] (Walton GH. Generating transition probabilities for Markov chain usage models. PhD Thesis, University of Tennessee, Knoxville, TN, May 1995.). In contrast, this paper is targeted at the software engineering practitioner, software development manager, and test manager. This paper also adds to the published literature on Markov chain usage modeling and model‐based testing by describing and illustrating an iterative process for usage model development and optimization and by providing some recommendations for embedding model‐based testing activities within an incremental development process. Copyright © 2000 John Wiley & Sons, Ltd.     

基于Monte Carlo方法的自适应多模型诊断

多模型混合系统的模型切换服从有限状态的Markov链,其转移概率通常假定是已知的.当模型转移概率未知的时候,本文基于Monte Carlo粒子滤波器给出了混合系统状态估计的一种自适应算法.该算法假定未知的转移概率先验分布为Dirichlet分布,首先通过采样得到一组模型序列的随机样本,利用其中状态的转移次数计算先验转移概率,使用量测信息对样本更新选择后,获得模型转移概率的一种迭代的后验估计值,同时由粒子滤波器得到系统状态和模型概率的后验估计.将该方法用于混合系统的状态监测和故障诊断,仿真结果表明了算法的有效性.     

Partially‐observable stochastic hybrid systems (poshss) state estimation and optimal control

This paper discusses the state estimation and optimal control problem of a class of partially‐observable stochastic hybrid systems (POSHS). The POSHS has interacting continuous and discrete dynamics with uncertainties. The continuous dynamics are given by a Markov‐jump linear system and the discrete dynamics are defined by a Markov chain whose transition probabilities are dependent on the continuous state via guard conditions. The only information available to the controller are noisy measurements of the continuous state. To solve the optimal control problem, a separable control scheme is applied: the controller estimates the continuous and discrete states of the POSHS using noisy measurements and computes the optimal control input from the state estimates. Since computing both optimal state estimates and optimal control inputs are intractable, this paper proposes computationally efficient algorithms to solve this problem numerically. The proposed hybrid estimation algorithm is able to handle state‐dependent Markov transitions and compute Gaussian‐ mixture distributions as the state estimates. With the computed state estimates, a reinforcement learning algorithm defined on a function space is proposed. This approach is based on Monte Carlo sampling and integration on a function space containing all the probability distributions of the hybrid state estimates. Finally, the proposed algorithm is tested via numerical simulations.     

系统调用序列的Markov模型及其在异常检测中的应用

建立了计算机系统中系统调用序列的Markov模型，并在此模型的基础上提出了一种用于计算机异常检测的方法，文章利用统计方法分析进程中系统调用的发生情况，定义了一个依赖于状态转移概率的失配因子，并用它来计算失配率，由此判断被监视进程进行的操作是正常行为还是异常行为，文章还提出了一种基于遗忘因子的状态转移概率的更新算法。     

Measuring complexity and coverage of software specifications

Coverage testing in the context of Markov chain usage models refers to coverage of a model of the specification and profile of intended use, rather than coverage of the code that implements the specification. A new measure of specification complexity based on the number of statistically typical paths through the model of the specification is derived. Formulae are presented to compute bounds on the expected number of test cases required to achieve state and arc coverage. Formulae are presented to compare different usage models with respect to the amount of testing required to achieve coverage of typical paths. Convexity properties are established for these formulae to facilitate their use in optimization calculations that are used to generate transition probabilities for the usage models.     

基于UML的软件Markov链使用模型的构建

构建软件的使用模型是进行软件可靠性测试及软件可靠性评估的基础.近年来,如何由软件的UML模型构造软件的使用模型成为研究热点.对于大型的软件系统来说,应用现有方法构建的软件Markov链使用模型的状态空间过于庞大,模型描述困难,不利于测试用例的自动生成及软件可靠性评估.针对以上问题,提出了一种由UML模型构建Markov链使用模型的方法.该方法将场景的前置条件和后置条件作为Markov链使用模型的状态,将场景的执行及执行概率作为状态之间的转移及转移概率.与现有方法相比,新方法构建的Markov链使用模型的状态空间小且无需人为干预,而且可以很方便地生成测试输入从而进行可靠性测试.针对UML模型的有效性,提出了经过可靠性评估扩展的UML模型生成Markov链使用模型的验证算法.最后通过一个卫星控制系统的实例对新方法的性能进行了验证.     

Robust probabilistic planning with ilao

In probabilistic planning problems which are usually modeled as Markov Decision Processes (MDPs), it is often difficult, or impossible, to obtain an accurate estimate of the state transition probabilities. This limitation can be overcome by modeling these problems as Markov Decision Processes with imprecise probabilities (MDP-IPs). Robust LAO* and Robust LRTDP are efficient algorithms for solving a special class of MDP-IPs where the probabilities lie in a given interval, known as Bounded-Parameter Stochastic-Shortest Path MDP (BSSP-MDP). However, they do not make clear what assumptions must be made to find a robust solution (the best policy under the worst model). In this paper, we propose a new efficient algorithm for BSSP-MDPs, called Robust ILAO* which has a better performance than Robust LAO* and Robust LRTDP, considered the-state-of-the art of robust probabilistic planning. We also define the assumptions required to ensure a robust solution and prove that Robust ILAO* algorithm converges to optimal values if the initial value of all states is admissible.