一个基于响铃形故障检测率函数的软件可靠性增长模型

非齐次泊松过程类软件可靠性增长模型是评价软件产品可靠性指标的有效工具．影响软件可靠性增长模型评估和预测准确性的最重要的两个因素是软件中隐藏的初始故障数和故障检测率．一些非齐次泊松过程类模型假设故障检测率是不随测试时间变化的常量，有些模型假设故障检测率是增函数或减函数．这些假设或忽略了测试者的学习过程，或忽略了越迟被检测到的故障的概率就可能越低的特点．该文将测试者的学习过程和软件固有故障检测率的变化特征相结合，提出了一个铃形的故障检测率函数，建立了一个非齐次泊松过程类软件可靠性增长模型——Bbell—SRGM．在一组失效数据上的实验分析表明：对这组失效数据，Bbell—SRGM模型比G-O模型等的拟合效果更好．     

考虑故障排除率和引入率的软件可靠性建模

软件测试中排错过程的故障排除率并不是100％的,并且由于检测到的尚未被排除的故障影响还会引入新的故障,即存在故障排除率和故障引入率,两者是具有不同的下降趋势的减函数.针对上述问题,提出一种新的非齐次泊松过程软件可靠性增长模型,考虑了随时间变化的故障排除率函数和故障引入率函数.利用一组公开发表的包含故障检测数和故障排除数的软件失效数据集进行仿真与验证,实验结果表明,改进模型具有更好的拟合效果和预测能力.     

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...     

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。     

一个NHPP类软件可靠性增长模型框架

NHPP类软件可靠性增长模型已经成为软件可靠性工程实践中非常成功的工具，从某些模型的一些共同特征出发，研究了NHPP类软件可靠性增长模型的有限通用框架，提出了一 个既考虑软件测试的不完美性、故障检测率随时间的变化，又考虑了故障改正效率随时间变化的NHPP类软件可靠性增长模型框架。一些已经存在的NHPP类软件可靠性增长模型型是这个框架的特例。     

考虑测试环境和实际运行环境的软件可靠性增长模型

软件可靠性增长模型中测试阶段和操作运行阶段环境的不同导致了两个阶段故障检测率的不同.非齐次泊松过程类软件可靠性增长模型是评价软件产品可靠性指标的有效工具.在一些非齐次泊松过程类模型中,有些学者提出了常量的环境因子,用来描述测试环境和运行环境的差别.实际上,环境因子应该是随时间变化的变量.考虑了运行阶段和测试阶段环境的不同,根据实测数据得到了变化的环境因子,并且根据测试阶段的故障检测率和变化的环境因子,转化得到了操作运行阶段的故障检测率.考虑到故障的排除效率和故障引入率,从而建立了一个既考虑运行环境和测试环境差别,又考虑故障排除效率和故障引入率的非齐次泊松过程类软件可靠性增长模型（PTEO-SRGM）.在两组失效数据上的实验分析表明,对这组失效数据,PTEO-SRGM模型比G-O模型等模型的拟合效果和预测能力更好.     

考虑非理想排错过程的软件可靠性模型

经典的可靠性模型排错过程是理想的,没有考虑各种实际情况.实际的排错过程并不是完美的,错误排除需要时间,且不可能完全排除,排错过程中可能引入新的错误,错误排除率和错误引入率均是随时间变化的函数等等.文中针对这些排错过程的实际情况,对Xuemei Zhang等人提出的软件可靠性模型进行了改进,提出新的假设,建立改进的新模型,给出模型的一般表达形式.并通过两组公开发表的失效数据,对改进后模型的一个特例模型的拟合预测能力进行仿真分析比较,最终验证了改进的考虑非理想排错过程的软件可靠性模型的优越性,说明了新模型更符合现实的软件可靠性活动过程.     

考虑不完美排错情况的NHPP 类软件可靠性增长模型

针对现有NHPP 类软件可靠性增长模型对故障排错过程中不完美排错情况考虑不完全的现状,提出了一 种新的软件可靠性增长模型.该模型全面考虑了不完美排错的两种情况:既考虑了排错过程中引入新错误的可能性, 又考虑了不完全排错的情况,并且引入了一种故障排除率随时间变化的故障排除率函数,使模型更符合实际情况.利 用公开发表的两组不同的软件失效数据对该模型进行验证的结果表明,与现有的对不完美排错情况考虑不完全的 模型相比,该模型能够取得更好的拟合结果和预测效果.     

考虑不完美排错情况的NHPP 类软件可靠性增长模型

针对现有NHPP类软件可靠性增长模型对故障排错过程中不完美排错情况考虑不完全的现状,提出了一种新的软件可靠性增长模型.该模型全面考虑了不完美排错的两种情况:既考虑了排错过程中引入新错误的可能性,又考虑了不完全排错的情况,并且引入了一种故障排除率随时间变化的故障排除率函数,使模型更符合实际情况.利用公开发表的两组不同的软件失效数据对该模型进行验证的结果表明,与现有的对不完美排错情况考虑不完全的模型相比,该模型能够取得更好的拟合结果和预测效果.     

故障察觉率可变的软件可靠性增长模型

证明了基于G-O模型的NHPP类型的软件可靠性增长模型不需要考虑不完美排错和排错过程中新错误的引入,并在该基础上提出了一种新的软件可靠性增长模型。该模型在软件排错过程中不但考虑了软件开发员对系统熟悉程度的上升,而且考虑了系统现存错误数的不断减少,是一种故障检测率随时间变化的软件可靠性增长模型。并利用现有的公开发表的数据对该模型进行测试,发现其达到了比G-O模型的等其他模型更好的拟合效果。     

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.     

故障检测率不规则变化的软件可靠性模型

传统的NHPP(non-homogeneous Poisson process)模型在实际的测试当中被证明是成功的.但是,由于传统的NHPP模型用的是理想的假设,例如,假设故障检测率是常数、平稳变化和规律变化,模型的性能在实际的测试环境中总是受到损害.因此,提出一个基于NHPP的软件可靠增长模型,并且考虑故障检测率的不规则变化情况,这种变化符合故障检测率在实际的软件测试过程中的变化.通过相关的实验验证了所提出的NHPP模型的拟合和预测能力.实验结果表明:在用实际的故障数据进行拟合和预测的过程中,所提出的模型与传统的NHPP模型相比,有更好的拟合和预测性能.同时,也给出了所提出模型相应的置信区间.     

基于软件层次化模型的软件测试数据生成

针对基于Markov链模型的软件测试技术在测试数据生成时不考虑软件的结构信息,生成的测试数据集对代码路径的覆盖能力以及缺陷检测能力都较低的问题,将统计测试与基于Markov链模型的测试相结合,提出了一种新的软件测试模型——软件层次化模型。该模型涵盖了软件与外部环境之间的交互,同时描述了软件内部结构信息。还给出了该模型测试数据集的生成算法：首先生成符合使用情况的测试序列,然后为测试序列生成覆盖软件内部结构的输入数据。通过针对示例软件的实验结果表明,与基于Markov链模型的测试方法对比,基于软件层次化模型的测试在满足软件测试充分性要求的同时,提高了测试数据集的代码路径覆盖能力和缺陷检测能力。     

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。     

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.     

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.