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1.
基于Logistic测试覆盖率函数的软件可靠性建模研究   总被引:1,自引:0,他引:1  
软件测试覆盖率是测试充分性和测试效率的有效度量指标,其与软件可靠性以及缺陷覆盖情况之间有着一定的相关关系,并且结合测试覆盖率信息的软件可靠性模型的评估和预计效果将会得到有效改进.在实际测试过程中,由于软件结构特征及学习因素的综合影响,测试覆盖率可能会呈现出一种先增后减的趋势,Logistic函数恰好非常适合描述这类S形变化趋势,且结构简单,具有较好的灵活性与适应性.因此,针对基于Logistic函数的测试覆盖率函数以及软件可靠性建模等问题展开研究.首先提出基于Logistic函数的测试覆盖率函数;在该函数的基础上,提出基于Logistic测试覆盖函数的缺陷预计模型;然后,将NHPP可靠性模型的建模过程与Logistic测试覆盖函数相结合,提出一种新的者虑测试覆盖率的软件可靠性增长模型.实例验证结果表明:与若干已有的同类研究成果相比,提出的基于Logistic函数的测试覆盖率函数、缺陷预计模型以及软件可靠性增长模型有效地提高了函数或模型对数据的拟和精度,且具有较好的适用性.  相似文献   

2.
现有的基于测试覆盖率的非齐次泊松过程(NHPP)类软件可靠性增长模型绝大多数都没有考虑到潜伏故障点不完美覆盖的情况。提出了一种考虑潜伏故障点不完美覆盖的软件可靠性NHPP增长模型,称之为UPNHPP模型。在一组失效数据上的实验分析表明,对这组数据,UPNHPP模型与其他模型相比有更好的拟合效果。  相似文献   

3.
对现有NHPP类软件可靠性模型进行分析总结,指明了已有NHPP类软件可靠性模型存在的不足及缺陷。综合考虑缺陷探测率、软件运行覆盖率、排除错误时的错误引入率等软件故障数的影响因素,提出了一种通用的NHPP类软件可靠性模型,最后对通用模型中的泛函数取特殊值后,求得期望故障数及软件可靠度,并对其进行分析,证明了所提模型的有效性。  相似文献   

4.
一个NHPP类软件可靠性增长模型框架   总被引:6,自引:0,他引:6  
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。  相似文献   

5.
考虑软件不同失效过程偏差的软件可靠性模型   总被引:3,自引:0,他引:3  
软件可靠性分析是根据软件失效数据等信息,通过合理建模来对软件可靠性进行预计和评价.现有的基于随机过程的可靠性模型一般采用均值过程来描述软件失效数据,然而,软件失效数据的模型化实质上应该是使其成为某个随机过程的一个样本轨迹.文中建立了考虑软件不同失效过程偏差的软件可靠性模型,用NHPP过程表示失效过程均值函数的变化趋势,ARMA过程表示实际失效过程对均值过程的偏差序列.在两组公开发表的真实数据集上对模型的实验表明,新模型较之一些广泛使用的NHPP软件可靠性模型在拟合能力及适用性上有明显的提高,并且保持了较好的预测能力.  相似文献   

6.
考虑不完美排错情况的NHPP 类软件可靠性增长模型   总被引:5,自引:0,他引:5  
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.  相似文献   

7.
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。  相似文献   

8.
考虑不完美排错情况的NHPP 类软件可靠性增长模型   总被引:1,自引:0,他引:1  
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.  相似文献   

9.
软件可靠性增长模型SRGM可对测试与运行阶段的可靠性进行度量、预测与保证。不完美排错SRGM能够更加准确地建模实际测试过程,获得了广泛研究。首先介绍了随机过程类模型中的NHPP基本概念与假设。接着,从三个阶段全面回顾了不完美排错研究历程。进一步,给出了若干典型的不完美排错SRGM的建模与累计故障检测函数的求解形式。最后将从排错的不完全性,引入新故障的角度建立的不完美排错模型:IID-SRGM与现有的模型进行比较,优于其它模型。  相似文献   

10.
非齐次泊松过程类软件可靠性增长模型(NHPP-SRGMs)是评价软件产品可靠性指标的有效工具,但大多数该类模型都未考虑软件缺陷关联这一测试过程中普遍存在的现象。该文在考虑软件缺陷关联关系的基础上对缺陷进行分类,提出一个改进的NHPP类软件可靠性增长模型。在一组失效数据上的实验分析表明,改进的模型具有较好的拟合效果和预测能力。  相似文献   

11.
考虑测试环境和实际运行环境的软件可靠性增长模型   总被引:6,自引:0,他引:6  
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.  相似文献   

12.
考虑故障相关的软件可靠性增长模型研究   总被引:3,自引:0,他引:3  
赵靖  张汝波  顾国昌 《计算机学报》2007,30(10):1713-1720
软件可靠性增长模型是用来评估和预测软件可靠性的重要工具.目前,绝大多数的软件可靠性增长模型并没有考虑故障之间的相关性,也没有考虑测试环境和运行环境的区别.文中提出了一种随机过程类非齐次泊松过程(NHPP)中的考虑故障相关性、测试环境和运行环境差别的模型.在两组失效数据上的实验分析表明:对这两组失效数据,文中提出的模型比其他一些非齐次泊松过程类模型的拟合效果和预测效果更好.  相似文献   

13.
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.  相似文献   

14.
Software testing is necessary to accomplish highly reliable software systems. If the project manager can conduct well-planned testing activities, the consumption of related testing-resources will be cost-effective. Over the past 30 years, many software reliability growth models (SRGMs) have been proposed to estimate the reliability growth of software, and they are mostly applicable to the late stages of testing in software development. Thus far, it appears that most SRGMs do not take possible changes of testing-effort consumption rates into consideration. However, in some cases, the policies of testing-resource allocation could be changed or adjusted. Thus, in this paper, we will incorporate the important concept of multiple change-points into Weibull-type testing-effort functions. The applicability and performance of the proposed models are demonstrated through two real data sets. Experimental results show that the proposed models give a fairly accurate prediction capability. Finally, based on the proposed SRGM, constructive rules are developed for determining optimal software release times.  相似文献   

15.
Generalized methods for software reliability growth modeling have been proposed so far. But, most of them are on continuous-time software reliability growth modeling. Many discrete software reliability growth models (SRGM) have been proposed to describe a software reliability growth process depending on discrete testing time such as the number of days (or weeks); the number of executed test cases. In this paper, we discuss generalized discrete software reliability growth modeling in which the software failure-occurrence times follow a discrete probability distribution. Our generalized discrete SRGMs enable us to assess software reliability in consideration of the effect of the program size, which is one of the influential factors related to the software reliability growth process. Specifically, we develop discrete SRGMs in which the software failure-occurrence times follow geometric and discrete Rayleigh distributions, respectively. Moreover, we derive software reliability assessment measures based on a unified framework for discrete software reliability growth modeling. Additionally, we also discuss optimal software release problems based on our generalized discrete software reliability growth modeling. Finally, we show numerical examples of software reliability assessment by using actual fault-counting data  相似文献   

16.
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.  相似文献   

17.
Software reliability testing refers to various software testing activities that are driven to achieve a quantitative reliability goal given a priori or lead to a quantitative reliability assessment for the software under test. In this paper we develop a modeling framework for the software reliability testing process, comprising a simplifying model and a generalized model. In both models the software testing action selection process and the defect removal mechanism are explicitly described. Both the discrete-time domain and the continuous-time domain are involved. The generalized model is more accurate or realistic than the simplifying model since the former avoids the assumption that defects are equally detectable and the assumption that defects are removed upon being detected. However simulation examples show that the simplifying model really captures some of essential features of the software testing process after a short initial testing stage. The modeling framework is practically realistic, mathematically rigorous, and quantitatively precise. It demonstrates that the relationship between software testing and delivered software reliability, which was poor understood, can well be formulated and quantified. Rigorous examinations show that several common assumptions adopted in software reliability modeling, including the independence assumption, the exponentiality assumption, and the NHPP assumption, are theoretically false in general. This paper sets a good starting point to further formalize and quantify the software testing process and its relation to delivered software reliability.  相似文献   

18.
It is widely believed in software reliability community that software reliability growth behavior follows a non-homogeneous Poisson process (NHPP) based on analyzing the behavior of the mean of the cumulative number of observed software failures. In this paper we present two controlled software experiments to examine this belief. The behavior of the mean of the cumulative number of observed software failures and that of the corresponding variance are examined simultaneously. Both empirical observations and statistical hypothesis testing suggest that software reliability behavior does not follow a non-homogeneous Poisson process in general, and does not fit the Goel–Okumoto NHPP model in particular. Although this new finding should be further tested on other software experiments, it is reasonable to cast doubt on the validity of the NHPP framework for software reliability modeling. The importance of the work presented in this paper is not only for the new finding which is distinctly different from existing popular belief of software reliability modeling, but also for the adopted research approach which is to examine the behavior of the mean and that of the corresponding variance simultaneously on basis of controlled software experiments.  相似文献   

19.
20.
基因表达式编程在软件可靠性建模中的应用   总被引:2,自引:0,他引:2       下载免费PDF全文
基因表达式编程是一种基于遗传算法和遗传编程的新型机器学习技术,其具有更为优秀的数据挖掘能力,已被成功应用于函数发现领域。提出一种基于基因表达式编程的非参软件可靠性建模方法,该方法将基因表达式编程算法中的若干关键步骤(如初始种群函数集、适应度函数、终止条件等)与软件可靠性建模的若干重要特征相融合,在失效数据集上进行训练,从而获得基于基因表达式编程算法的非参软件可靠性模型。在若干组真实失效数据集上,将所提出的模型与若干典型的基于人工神经网络以及遗传编程的非参软件可靠性模型进行对比实例研究。实例结果表明,基因表达式编程算法的非参软件可靠性模型具有更为显著的模型拟合与预计性能。  相似文献   

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