基于故障等级分析的软件可靠性评估方法研究

介绍了一种基于故障等级分析的软件可靠性评估方法,这种方法利用了软件系统测试后得到的缺陷数据,对每一个缺陷导致失效的内外因进行分析,算出每个缺陷的失效率。将失效数据进行分级,在失效率小的等级中进行故障注入,试验结果表明,该方法将软件可信性定位在正确性、可靠性、容错性、安全性、隐私性,通过这五个要素的评估。评估的有效性和合理性不亚于传统的方法,大大提高了软件开发的质量和效率。     

舰船装备备件配置需求量的评估方法

研究舰船装备备件准确评估问题,由于舰船装备备件受到使用方式、使用环境、使用者、储存条件等多个因素的影响,实际消耗数据具有不确定性,传统的方法并不适用于全部的舰船装备备件配置需求量的计算.在分析了舰船装备的需求规律和使用可靠性信息的基础上,建立了失效数的统计模型,将备件失效率分为工作失效率和非工作失效率,提出了备件实际消耗数据的失效率的Bayes评估方法,充分利用了装备的各类可靠性历史信息和专家经验信息,更适用于装备批量小或工作时见短的舰船装备.给出了舰船装备在满足一定保障度下的备件配置需求量的确定方法.经验证实例说明方法是可行的.     

基于缺陷的软件测试性评估方法分析

为了对软件测试过程中发现缺陷能力的进行有效的度量,研究了基于缺陷的软件测试性评估方法。从缺陷导致失效的过程出发,描述了基于缺陷的软件测试性评估模型和评估方法,并对该方法针对运算量大以及缺陷分布不合理问题而衍生的改进方法进行了综述。深入的分析和比较了各方法的原理、应用和局限性,并对软件测试性评估的应用方向进行了探讨和展望。     

无失效数据情形船舶E-Bayes可靠性分析

为了解决造价昂贵以及高可靠性产品的可靠性评估问题,基于船舶无失效数据情形,提出了船舶寿命的可靠性评估方法。根据E-Bayes(Expected-Bayesian)理论,设船舶寿命服从指数分布,在失效率的先验分布为Gamma分布,且两个超参数选择三种不同的先验分布情况下,给出船舶失效率和可靠度的三种E-Bayes估计模型;对比分析三种模型,给出EBayes估计的渐进性质。结合集装箱5 600船舶(标箱)寿命无失效数据进行计算,结果表明本文提出的船舶可靠性的E-Bayes估计模型可行、便于工程应用,其中第二种模型当两个超参数同时选取均匀分布时,估计结果更稳健更保守。     

基于失效机理的软件测试性评估方法研究

软件测试性是在测试过程中发现缺陷能力的度量指标。基于缺陷导致失效的机理,通过对主要的测试性评估方法分析和研究,总结出一套有实用价值的基于失效机理的软件测试性评估方法,并提出了该方法在软件度量和软件质量改善两个方面的应用前景。     

基于主观估计法和FMEDA的失效数据分析及应用

针对安全仪表系统安全完整性等级验证（SIL）和机械装置的失效模式、影响及诊断分析（FMEDA）缺乏特定工厂环境、联锁回路中的产品（如阀门、变送器、逻辑控制器等）失效数据这一关键不足,提出了一种基于主观估计法和皮尔逊卡方检验法的失效数据分析方法,并以丙烯腈反应器出口温度高联锁回路中的气动薄膜调节阀为例,应用该方法估计产品寿命分布和实际失效率;应用机械FMEDA法和故障树（FTA）法对回路进行SIL验证,给出改进措施。结果表明,应用该方法获取的产品失效数据对相应联锁回路进行SIL验证比应用国外通用数据库更准确。     

XML过滤中缓存失效策略的性能分析数学模型

硬件缓存行为是内存驻留的数据密集型系统(例如XML过滤机制)的一个重要特征.目前对XML的过滤方式的主流研究都是用自动机来表达主存中长期运行的XML查询.现主要通过分析性的建模和系统度量来研究基于自动机的XML过滤的cache性能,将原本笼统地针对整个cache失效性的分析细化成建立cache圈内模型和跨圈模型来估计cache的失效率,并通过实验证明该评估机制具有较高的精确度.     

安全仪表系统安全完整性等级验证研究进展

安全仪表系统对于工业生产安全意义重大.IEC 61508要求对所有的安全仪表系统确定安全完整性等级,以验证其是否满足功能安全的需求.过程工业中常见的是低要求模式的安全仪表系统.本文主要对这类系统安全完整性等级验证的相关研究进展进行了回顾,总结了共因失效建模和平均需求时失效率算法的研究.在平均需求时失效率算法方面,本文介绍了目前常见的3种方法——可靠性框图、故障树分析和Markov模型.本文的最后部分还介绍了几种安全完整性等级验证相关的商业软件.     

基于失效分析的软件可靠性评估方法研究

基于用户运行剖面的软件可靠性测试理论广泛地应用在软件测试领域.然而,这种软件可靠性测试既耗时又昂贵.在国内,现在进行的软件黑盒测试主要是系统测试,对于高可靠性要求的软件,系统测试后也需要评估它的可靠性水平.介绍了一种基于失效分析的软件可靠性评估方法,这种方法利用了软件系统测试后得到的缺陷数据,对每一个缺陷导致失效的内外...     

有信息丢失样本的一种数据处理方法：改进残存比率法

本文从理论上分析了残存比率法的缺陷,并对失效比率公式作了改进,提出了区间平均失效率的概念,并建立了有关公式。本文用实例说明了改进残存比率法的正确性,阐明了在确定产品可靠寿命或翻修时限时尽可能用区间平均失效率代替可靠度的理由和优越性。     

On the estimation of adequate test set size using fault failure rates

Test set size in terms of the number of test cases is an important consideration when testing software systems. Using too few test cases might result in poor fault detection and using too many might be very expensive and suffer from redundancy. We define the failure rate of a program as the fraction of test cases in an available test pool that result in execution failure on that program. This paper investigates the relationship between failure rates and the number of test cases required to detect the faults. Our experiments based on 11 sets of C programs suggest that an accurate estimation of failure rates of potential fault(s) in a program can provide a reliable estimate of adequate test set size with respect to fault detection and should therefore be one of the factors kept in mind during test set construction. Furthermore, the model proposed herein is fairly robust to incorrect estimations in failure rates and can still provide good predictive quality. Experiments are also performed to observe the relationship between multiple faults present in the same program using the concept of a failure rate. When predicting the effectiveness against a program with multiple faults, results indicate that not knowing the number of faults in the program is not a significant concern, as the predictive quality is typically not affected adversely.     

基于WPT和FOAGRNN的模拟电路故障诊断

为提高对模拟电路故障模式的准确分类和减少网络模型的训练时间,提出基于小波包变换(WPT)和果蝇算法(FOA)优化广义回归神经网络(GRNN)的模拟电路故障诊断方法。首先采用小波包变换提取电路优质故障特征,以减少网络训练时间,然后建立GRNN网络模型,选择FOA算法优化GRNN网络参数,构建最优模型对电路故障特征进行训练测试,最后采用仿真测试其性能。实验结果表明,FOA算法有效提高诊断模型训练效率,相比于其它电路故障诊断模型,FOAGRNN模型具有更高的诊断率和优越性。     

软件缺陷的综合研究

软件缺陷的概念在软件质量范畴中处于举足轻重的地位，软件缺陷度量是软件质量度量范畴内的核心度量。该文区分了错误、缺陷、故障、失效4个软件缺陷相关的概念，采用正交缺陷分类法建立了软件缺陷的分类分级模式，剖析了软件缺陷的生存周期，给出了缺陷密度、缺陷泄漏矩阵、缺陷注入率和缺陷消除率等基本的、实用的软件缺陷度量。     

Visio故障树图并行转换方法研究及实现

为使不同人员建立的故障树图能够互相交流使用,并简化故障树分析软件的开发,降低开发成本,提高软件质量,对故障树图结构进行了分析,提出转换后用于表示故障树的格式。同时对VDX文件结构进行研究,提出了将VDX文件转换为上述格式的中间格式及转换算法,进而对其并行化以提高执行效率,给出了并行前后的耗时对比及提升倍率。另外再通过两个转换方法说明这种格式与目前较常用的故障树分析方法之间衔接的方式,并给出了算法流程。     

新型电子装备软件故障诊断方法研究

操作程序(软件)已成为新型电子装备的重要的组成部分．软件中存在的缺陷影响电子装备的战术与技术性能。为了检测和诊断电子装备操作软件故障．针对软件特点和故障特征．建立了软件故障诊断模型，分析了故障隔离方法，提出了基于故障报告信息的诊断策略。实践证明．该方法较好地解决了电子装备操作软件的故障诊断问题。     

基于代理的航天器自主运行仿真系统设计与验证系统设计

针对目前航天器自主健康管理功能测试过程中,由于故障模拟手段不足造成测试覆盖率低,测试项目不完备,测试效率低等问题,提出一种基于代理的航天器自主健康故障仿真验证系统设计方案;基于该方案实现的故障仿真系统支持根据通用化航天器自主健康故障检测模型,严格按逻辑和时序,无延迟、持续的向全实物或半实物测试系统自动注入故障状态表征参数,模拟航天器整器或任意分系统、单机、软件的故障状态,模拟弥补了长期以来在实物测试环境下,整器故障模式测试覆盖率低,测试用例复用性差的问题;实践证明,此方法能将测试覆盖率提升至95%以上,并将测试时间缩短至传统方式的1/6,有效提升被测航天器产品可靠性。     

Deriving a Fault Architecture to Guide Testing

Defect analysis of software components can be used to guide testing, with the goal of focusing on parts of the software that were fault-prone in earlier releases or earlier life cycle phases, such as development. We replicate a study that adapted a reverse architecting technique using defect reports to derive fault architectures. A fault architecture determines and visualizes components that are fault-prone in their relationships with other components, as well as those that are locally fault-prone. Our case study uses defect data from three releases of a large medical record system to identify relationships among system components, based on whether they are involved in the same defect report.We investigate measures that assess the fault-proneness of components and component relationships. Component relationships are used to derive a fault architecture. The resulting fault architecture indicates what the most fault-prone relationships are in a release. We also apply the technique in a new way. Not only do we derive fault architectures for each release, we derive fault architectures for the development, system test and post release phases within each release. Comparing across releases, makes it possible to see whether some components are repeatedly in fault-prone relationships. Comparing across phases, makes it possible to see whether development fault architectures can be used to identify those parts of the software that need to be tested more. We validate our predictions using system test data from the same release. We also use the development and system test fault architectures to identify fault-prone components after release, and validate our predictions using post release data.     

Predicting operational software availability and its applications to telecommunication systems

It is essential to predict customer-perceived software availability during software development and determine when to release the software to maintain a balance among time-to-market, development cost and software quality. This paper presents methods and procedures to predict software failure rates from a user perspective in system test phases and to reverse-engineer in order to estimate software release time for given availability targets. Software reliability analysis is conducted based on non-homogenous Poisson process models. Software system test data of current release are used to estimate the number of residual faults by the end of system tests and data of previous releases or similar products (including system test data, post-system test data and field failure data) provide a means to predict a user-perceived average failure rate of a fault. Software system availability can be predicted from these estimates. Both execution and calendar times are considered. A software resource utilization model is developed to transfer one testing time to another. A telecommunications application illustrates how to calculate the failure rate and testing time to meet the software availability requirements.     

Software Quality Analysis of Unlabeled Program Modules With Semisupervised Clustering

Software quality assurance is a vital component of software project development. A software quality estimation model is trained using software measurement and defect (software quality) data of a previously developed release or similar project. Such an approach assumes that the development organization has experience with systems similar to the current project and that defect data are available for all modules in the training data. In software engineering practice, however, various practical issues limit the availability of defect data for modules in the training data. In addition, the organization may not have experience developing a similar system. In such cases, the task of software quality estimation or labeling modules as fault prone or not fault prone falls on the expert. We propose a semisupervised clustering scheme for software quality analysis of program modules with no defect data or quality-based class labels. It is a constraint-based semisupervised clustering scheme that uses k-means as the underlying clustering algorithm. Software measurement data sets obtained from multiple National Aeronautics and Space Administration software projects are used in our empirical investigation. The proposed technique is shown to aid the expert in making better estimations as compared to predictions made when the expert labels the clusters formed by an unsupervised learning algorithm. In addition, the software quality knowledge learnt during the semisupervised process provided good generalization performance for multiple test data sets. An analysis of program modules that remain unlabeled subsequent to our semisupervised clustering scheme provided useful insight into the characteristics of their software attributes