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.     

一种软件可靠性增长模型选择与综合方法

软件可靠性增长模型可以预测软件在将来某个时刻的可靠性,以此作为软件是否发布的依据.而目前常见的各种模型对不同失效数据集的预测能力并不一致.提出了一种软件可靠性增长模型选择和应用的框架,利用可靠性模型评价准则,对特定的失效数据集选择优选模型集,根据优选模型集利用神经网络较好的学习预测能力计算可靠性.利用此方法对实际软件项目中的失效数据进行了分析,并验证了它的有效性.     

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。     

基于聚类思想的软件可靠性模型选择

软件可靠性模型应用的不一致性一直是困扰软件可靠性研究人员的主要问题。模型选择和组合策略作为主要解决方法已成为模型应用的研究重点。该文主要探讨运用聚类思想进行软件可靠性模型选择的方法。根据针对实际失效数据的可靠性模型评价准则值编码,该文采用高斯混合模型进行聚类分析,使用EM算法估计高斯混合模型的参数,贝叶斯准则进行模型选择。为验证此方法的有效性和可行性使用了多个实际项目中的失效数据进行试验。结果表明,此模型选择方法简单有效,有利于软件可靠性模型应用不一致性问题的解决。     

级联网络的软件可靠性预测

研究软件可靠性准确预测问题,软件存在动态失效性,且引起软件运行失效的原因具有随机性,不同可靠性模型预测相同软件得到的结果不一致,通用性比较差,导致预测精度低。为了提高软件可靠性预测精度,提出一种级联网络的软件可靠性预测模型。采用4种经典软件可靠性模型的输出作为BP神经网络模型的输入,利用各种单一预测模型的优点,建立一种新的级联软件可靠性模型。仿真结果表明,级联网络模型具有更高的预测精度和通用性,验证了级联网络预测模型对软件可靠性预测的有效性和良好的应用前景。     

高可靠软件的可靠性评估技术

研究了高可靠软件的可靠性评估技术,给出了失效数据稀少情况下的软件可靠性模型.假设高可靠软件的可靠性测试过程中发生失效是独立同分布的稀有事件,从理论上分析了极值统计理论用于软件可靠性评估的可行性,建立了软件可靠性极值统计模型,讨论了模型的参数估计方法和假设检验方法.     

基于未确知理论的软件可靠性建模

将未确知理论应用于软件可靠性建模研究,采用其分析软件故障过程,用未确知数学描述软件失效特征计算软件可靠性参数,并在此基础上构建了一个基于未确知数学理论的软件可靠性模型.新模型改变了传统的建模思路,跳出了传统软件可靠性建模过程中关于失效强度变化的各种统计分布假设的束缚,具有较好的适用性,改善了模型应用中的不一致性问题.     

考虑系统调用和失效模式的软件可靠性模型

可靠性是软件系统质量需求中的一个重要指标,而软件可靠性建模为人们提供了一种评估软件可靠性的方法,能够在软件质量控制方面有效地提供决策依据。无论是曾经受到广泛关注的黑盒方法,还是现在大量研究的白盒方法,这些模型都试图越来越精确地刻画软件的可靠性。然而,这些模型都是将软件独立在运行环境之外,只是纯粹地、抽象地考虑软件本身,完全忽略了软件所依赖的操作系统和运行环境对可靠性的影响。将软件运行所依赖的系统调用纳入了考虑范畴,考虑了软件中的多种失效模式（内核态失效、用户态失效）,同时对系统中的错误传播行为进行了建模,经过对经典模型逐步扩展,建立了一个更加符合实际情况的软件可靠性模型。通过一个实例详细描述了该模型的如何应用。     

故障检测率对软件可靠性影响实证分析

故障检测率是软件可靠性模型的主要参数之一,不同形式的故障检测率具有不同的作用。聚焦于故障检测率对软件可靠性的影响,提出基于信息熵与优劣距离决策算法的单可靠性模型单失效数据集多故障检测率与多可靠性增长模型多失效数据集多故障检测率2种实证分析方案,旨在全面地分析故障检测率的影响。经过实验分析,对于单一可靠性模型单一数据集,故障检测率对软件可靠性的影响主要与失效数据集相关,在不同数据集上不同故障检测率函数的性能差异较大;在多可靠性模型多数据集上,幂函数与S型故障检测率对应的软件可靠性模型的综合性能较好,指数型故障检测率对应的软件可靠性模型的综合性能较差。本文的研究对于软件可靠性建模中的模型参数选择、最优发布时间的确定等具有较强的指导作用。     

基于Bayes的软件可靠性模型研究

随着软件质量的提高,对软件可靠性的要求也越来越高。目前已经有一百多种软件可靠性模型。然而,在实际应用中由于可操作性等因素的影响,大多模型都没法得到有效的应用。鉴于此,本文在Nelson模型的基础上,结合Bayes原理给出了一种新的衡量软件可靠性的简单方法。实验表明,改进后的模型在实际应用中有较好的效果。     

Imperfect debugging models with fault introduction rate for software reliability assessment

In general it is considered to be unrealistic in software reliability modelling to assume that the faults detected by software testing are perfectly removed without introducing new faults. In this paper we propose two software reliability assessment models with imperfect debugging by assuming that new faults are sometimes introduced when the faults originally latent in a software system are corrected and removed during the testing phase. It is assumed that the fault detection rate is proportional to the sum of the numbers of faults remaining originally in the system and faults introduced by imperfect debugging. These two models are described by a nonhomogeneous Poisson process. Several quantitative measures for reliability assessment are derived, and the maximum likelihood estimations of unknown model parameters are presented. Finally, numerical examples of software reliability analysis based on these two models are shown.     

软件可靠性Schneidewind模型的特性分析

文中对软件可靠性的Ｓｃｈｎｅｉｄｅｗｉｎｄ计时模型的参数估计进行了了解结构的理论分析，并在此基础上给出了方便实用的参数估计算法。     

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...     

Considering the fault dependency concept with debugging time lag in software reliability growth modeling using a power function of testing time

Since the early 1970s tremendous growth has been seen in the research of software reliability growth modeling.In general, software reliability growth models (SRGMs) are applicable to the late stages of testing in software development and they can provide useful information about how to improve the reliability of software products.A number of SRGMs have been proposed in the literature to represent time-dependent fault identification/removal phenomenon;still new models are being proposed that could fit a greater number of reliability growth curves.Often,it is assumed that detected faults axe immediately corrected when mathematical models are developed.This assumption may not be realistic in practice because the time to remove a detected fault depends on the complexity of the fault,the skill and experience of the personnel,the size of the debugging team,the technique,and so on.Thus,the detected fault need not be immediately removed,and it may lag the fault detection process by a delay effect factor.In this paper,we first review how different software reliability growth models have been developed,where fault detection process is dependent not only on the number of residual fault content but also on the testing time,and see how these models can be reinterpreted as the delayed fault detection model by using a delay effect factor.Based on the power function of the testing time concept,we propose four new SRGMs that assume the presence of two types of faults in the software:leading and dependent faults.Leading faults are those that can be removed upon a failure being observed.However,dependent faults are masked by leading faults and can only be removed after the corresponding leading fault has been removed with a debugging time lag.These models have been tested on real software error data to show its goodness of fit,predictive validity and applicability.     

Discrete software reliability growth modeling for errors of different severity incorporating change-point concept

Several software reliability growth models (SRGM) have been developed to monitor the reliability growth during the testing phase of software development.In most of the existing research available in the literatures,it is considered that a similar testing effort is required on each debugging effort.However,in practice,different types of faults may require different amounts of testing efforts for their detection and removal.Consequently,faults are classified into three categories on the basis of severity:simple,hard and complex.This categorization may be extended to (?) type of faults on the basis of severity.Although some existing research in the literatures has incorporated this concept that fault removal rate (FRR) is different for different types of faults,they assume that the FRR remains constant during the overall testing period.On the contrary,it has been observed that as testing progresses,FRR changes due to changing testing strategy,skill,environment and personnel resources.In this paper,a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept.Then,the models are formulated for two particular environments.The models were validated on two real-life data sets.The results show better fit and wider applicability of the proposed models as to different types of failure datasets.     

一种基于故障模型的代码静态测试方法研究

软件测试是排除软件故障,提高软件质量和可靠性的重要手段。基于故障模型的软件测试是软件编码阶段的主流测试方法之一。基于故障模型的代码静态测试技术具有测试效率高、对逻辑复杂故障测试效果好等特点。鉴于此,本文采取一种特殊的静态分析技术来实现对代码的测试。首先讨论传统软件测试方法的缺点和局限性,给出基于故障模型的静态测试方法的优越性;然后在分析过程中,综合应用抽象语法树和控制流图,提出一种基于故障模型的软件测试方法。依据该算法开发自动化测试工具,给出实验结果和对比分析,并指出下一步的研究方向。     

Ordering Fault-Prone Software Modules

Software developers apply various techniques early in development to improve software reliability, such as extra reviews, additional testing, and strategic assignment of personnel. Due to limited resources and time, it is often not practical to enhance the reliability of all modules. Our goal is to target reliability enhancement activities to those modules that would otherwise have problems later. Prior research has shown that a software quality model based on software product and process metrics can predict which modules are likely to have faults.A module-order model is a quantitative software quality model that is used to predict the rank-order of modules according to a quality factor, such as the number of faults. The contribution of this paper is definition of module-order models and a method for their evaluation and use. Two empirical case studies of full-scale industrial software systems provide empirical evidence of the usefulness of module-order models for targeting reliability enhancement.