Effective confidence interval estimation of fault-detection process of software reliability growth models

The quantitative evaluation of software reliability growth model is frequently accompanied by its confidence interval of fault detection. It provides helpful information to software developers and testers when undertaking software development and software quality control. However, the explanation of the variance estimation of software fault detection is not transparent in previous studies, and it influences the deduction of confidence interval about the mean value function that the current study addresses. Software engineers in such a case cannot evaluate the potential hazard based on the stochasticity of mean value function, and this might reduce the practicability of the estimation. Hence, stochastic differential equations are utilised for confidence interval estimation of the software fault-detection process. The proposed model is estimated and validated using real data-sets to show its flexibility.     

A risk-reduction approach for optimal software release time determination with the delay incurred cost

Most existing research on software release time determination assumes that parameters of the software reliability model (SRM) are deterministic and the reliability estimate is accurate. In practice, however, there exists a risk that the reliability requirement cannot be guaranteed due to the parameter uncertainties in the SRM, and such risk can be as high as 50% when the mean value is used. It is necessary for the software project managers to reduce the risk to a lower level by delaying the software release, which inevitably increases the software testing costs. In order to incorporate the managers’ preferences over these two factors, a decision model based on multi-attribute utility theory (MAUT) is developed for the determination of optimal risk-reduction release time.     

A dynamic software release model

The determination of optimal software release times constitutes an interesting decision making problem which involves the stochastic structure of the underlying software reliability model, as well as various cost parameters. There is an apparent tradeoff between testing the software further to improve its reliability, and releasing it for operational use to decrease the costs. We propose and analyze in depth a new dynamic model with sufficient generality. After each failure, a debugging activity, possibly imperfect, is undertaken and a decision is made regarding the duration of additional testing. If no failure is observed during this time, then the software is released. Otherwise, the failure is debugged and the decision process is repeated in a dynamic fashion. The problem is formulated using dynamic programming and interesting characterizations of the optimal release policy are presented. The dynamic solution procedure is demonstrated by some numerical illustrations.     

Software release management for component‐based software

Software release management is the process through which software is made available to and obtained by its users. Until now, this process has been relatively straightforward. However, the emergence of component‐based software is complicating software release management. Increasingly, software is constructed via the assembly of pre‐existing, independently produced, and independently released components. Both developers and users of such software are affected by these complications. Developers need to accurately document the complex and changing dependencies among the components constituting the software. Users must be involved in locating, retrieving, and assembling components in order to appropriately bring the software into their particular environment. In this paper, we introduce the problem of release management for component‐based software and discuss SRM, a prototype software release management tool we have developed that supports both developers and users in the software release management process. Copyright © 2002 John Wiley & Sons, Ltd.     

Reliability growth modeling and optimal release policy under fuzzy environment of an N-version programming system incorporating the effect of fault removal efficiency

Failure of a safety critical system can lead to big losses.Very high software reliability is required for automating the working of systems such as aircraft controller and nuclear reactor controller software systems.Fault-tolerant softwares are used to increase the overall reliability of software systems.Fault tolerance is achieved using the fault-tolerant schemes such as fault recovery (recovery block scheme),fault masking (N-version programming (NVP)) or a combination of both (Hybrid scheme).These softwares incorporate the ability of system survival even on a failure.Many researchers in the field of software engineering have done excellent work to study the reliability of fault-tolerant systems.Most of them consider the stable system reliability.Few attempts have been made in reliability modeling to study the reliability growth for an NVP system.Recently,a model was proposed to analyze the reliability growth of an NVP system incorporating the effect of fault removal efficiency.In this model,a proportion of the number of failures is assumed to be a measure of fault generation while an appropriate measure of fault generation should be the proportion of faults removed.In this paper,we first propose a testing efficiency model incorporating the effect of imperfect fault debugging and error generation.Using this model,a software reliability growth model (SRGM) is developed to model the reliability growth of an NVP system.The proposed model is useful for practical applications and can provide the measures of debugging effectiveness and additional workload or skilled professional required.It is very important for a developer to determine the optimal release time of the software to improve its performance in terms of competition and cost.In this paper,we also formulate the optimal software release time problem for a 3VP system under fuzzy environment and discuss a the fuzzy optimization technique for solving the problem with a numerical illustration.     

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.     

面向多用户软件可靠性评估和测试方案研究

软件可靠性估计是软件可靠性研究的重要问题之一。本文提出一种根据软件随机测试结果进行软件可靠性估计的方法，给出了软件可靠度的最优置信下限，并以此为基础对随机测试方案的优化设计进行了研究。     

软件众包开发者的能力价值率模型研究

为了更好地评价众包软件开发者的能力,分析了众包模式下软件众包开发的三个基本要素,即大众开发者、任务发布者、众包平台,提出了一种软件众包开发者能力价值率模型。该模型将软件众包任务从子任务、时间阶段、质量特性三个维度进行细分,首先评价众包原子单元,进而综合评估整个众包软件的质量。模型在评价过程中充分考虑了开发者的价值贡献因素,推演了开发者能力价值率的计算方法。验证实验表明软件众包开发者的综合能力价值率随着任务量的增加,其变化和能力大小关系趋于一致,所设计模型具有更好的稳定性,能够有效地评估软件众包开发者能力。     

Importance measures for modular software with uncertain parameters

Importance measures provide a sense of the relative priorities of components and can be used to guide the allocation of resources for cost‐effective improvement of system reliability starting from the early phases. Uncertainties in model parameters, prevalent in the early phases, can introduce errors into these measures, and hence, importance assessment must account for these parametric uncertainties. In this paper, a framework for importance assessment of a software system within the context of its architecture is proposed. The framework includes two methods; the first one systematically quantifies the confidence intervals in the model parameters, and the second one offers an analytical approach for importance analysis. The two methods in conjunction address the issue of importance assessment in the face of parametric uncertainties. Illustration of the framework using a banking application demonstrates the value and benefits of the methodology. Copyright © 2009 John Wiley & Sons, Ltd.     

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.     

Software reliability model with optimal selection of failure data

The possibility of obtaining more accurate predictions of future failures by excluding or giving lower weight to the earlier failure counts is suggested. Although data aging techniques such as moving average and exponential smoothing are frequently used in other fields, such as inventory control, the author did not find use of data aging in the various models surveyed. A model that includes the concept of selecting a subset of the failure data is the Schneidewind nonhomogeneous Poisson process (NHPP) software reliability model. In order to use the concept of data aging, there must be a criterion for determining the optimal value of the starting failure count interval. Four criteria for identifying the optimal starting interval for estimating model parameters are evaluated The first two criteria treat the failure count interval index as a parameter by substituting model functions for data vectors and optimizing on functions obtained from maximum likelihood estimation techniques. The third uses weighted least squares to maintain constant variance in the presence of the decreasing failure rate assumed by the model. The fourth criterion is the familiar mean square error. It is shown that significantly improved reliability predictions can be obtained by using a subset of the failure data. The US Space Shuttle on-board software is used as an example     

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.     

Reliability analysis and optimal version-updating for open source software

Context

Although reliability is a major concern of most open source projects, research on this problem has attracted attention only recently. In addition, the optimal version-dating for open source software considering its special properties is not yet discussed.

Objective

In this paper, the reliability analysis and optimal version-updating for open source software are studied.

Method

A modified non-homogeneous Poisson process model is developed for open source software reliability modeling and analysis. Based on this model, optimal version-updating for open source software is investigated as well. In the decision process, the rapid release strategy and the level of reliability are the two most important factors. However, they are essentially contradicting with each other. In order to consider these two conflicting factors simultaneously, a new decision model based on multi-attribute utility theory is proposed.

Results

Our models are tested on the real world data sets from two famous open source projects: Apache and GNOME. It is found that traditional software reliability models provide overestimations of the reliability of open source software. In addition, the proposed decision model can help management to make a rational decision on the optimal version-updating for open source software.

Conclusion

Empirical results reveal that the proposed model for open source software reliability can describe the failure process more accurately. Furthermore, it can be seen that the proposed decision model can assist management to appropriately determine the optimal version-update time for open source software.     

Reliability assessment based on hazard rate model for an embedded OSS porting‐phase

Embedded Open Source Software (OSS) systems have been gaining a lot of attention in the embedded system area. The successful experience of embedded OSSs include Android, BusyBox, TRON, etc. Also, OSS market will be expanded not only to mobile phone OSS areas but also to other embedded OSS areas in the future. However, the poor handling of quality problem and customer support prohibit the progress of embedded OSS. Also, it is difficult for developers to assess the reliability and portability of embedded OSS on a single‐board computer. A method of software reliability assessment based on flexible hazard rate modeling for the embedded OSS is proposed in this paper. Also, several numerical examples are shown by using actual data. Moreover, this paper compares the proposed model with the conventional hazard rate models by using the comparison criteria of goodness‐of‐fit. Copyright © 2011 John Wiley & Sons, Ltd.     

Integrated model for software component selection with simultaneous consideration of implementation and verification

One important objective of component-based software engineering is the minimization of the development cost of software products. Thus, the costs of software component implementation and verification, which may involve substantial expenses while under development, should be reduced. In addition, the costs for these processes should not be considered individually, but in an integrated manner, to further reduce development cost. In the current paper, an integrated decision model is proposed to assist decision-makers in selecting reuse scenarios for components used for implementation and in simultaneously determining the optimal number of test cases for verification. An objective of the model is the minimization of development cost, while satisfying the required system and reliability requirements. The Lagrange relaxation decomposition (LRD) method with heuristics was developed to solve integrated decision problems. Based on LRD, the nonlinear model is condensed into a 0–1 knapsack problem for the subproblem on reuse scenario selection and an integer knapsack problem for the subproblem on the determination of the optimal number of tests. Combined with the Lagrange multiplier-determined heuristic, the proposed algorithm can determine the global optimum solution. Simulations of varying sizes for problems and sensitivity analyses were conducted, and the results indicate that LRD is more effective than previous methods in determining global optimal solutions for the integrated decision problem.     

基于非马尔可夫随机Petri网的软件再生建模与分析

软件老化是影响软件系统可靠性的重要潜在因素,软件再生作为一种主动预防性的软件容错技术是解决软件老化问题的主要手段.以往的随机Petri网再生模型假定所有变迁的实施时间服从指数分布.针对变迁的实施时间服从确定性分布或一般性分布的情况,文中提出了一种用非马尔可夫随机Petri网建立软件再生模型的方法.该方法采用马尔可夫再生理论对模型进行分析,并给出模型的瞬态解和稳态解.仿真实验表明:选择合适的软件再生周期,可以有效地降低存在老化的软件系统的平均宕机成本,提高系统的可用性和可靠性.     

一种面向开发过程的软件可靠性预测方法

在进行软件开发时,该如何回答这样的问题,＂当前开发的软件可靠性是什么？＂这个对于用户来说是非常重要的信息,却难以表述。以此为方向,研究了一种面向开发过程的软件可靠性预测方法,结合软件开发各个阶段的特点,通过合理地采用软件可靠性早期预测模型,实现对开发中软件可靠性的预测。实践应用表明,该方法紧密结合软件开发的实际,反映了软件可靠性与开发人员的水平、开发规范的标准之间的联系,有助于采取必要的措施提高其以后的可靠性,从而也验证了该方法的工程实用性。     

基于机器学习理论的软件可靠性预测研究

由于软件可靠性早期预测在测试前就能够使开发和测试的相关人员对软件的可靠性有一定的了解,所以对于软件如何进一步开发、测试和质量的控制都具有十分重要的作用。该文将支持向量机理论引入到软件可靠性早期预测领域中来,提出了基于支持向量机的软件可靠性早期预测模型。通过对比仿真实验,证实了此模型同传统模型相比具有预测精度更高、泛化能力更强、对样本数量的依赖程度更低的特点。     

基于机器学习理论的软件可靠性预测研究

由于软件可靠性早期预测在测试前就能够使开发和测试的相关人员对软件的可靠性有一定的了解．所以对于软件如何进一步开发、测试和质量的控制都具有十分重要的作用。该文将支持向量机理论引入到软件可靠性早期预测领域中来,提出了基于支持向量机的软件可靠性早期预测模型。通过对比仿真实验,证实了此模型同传统模型相比具有预测精度更高、泛化能力更强、对样本数量的依赖程度更低的特点。     

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。