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.     

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.     

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.     

The determination of optimal software release times at different confidence levels with consideration of learning effects

As most software reliability models do not clearly explain the variance in the mean value function of cumulative software errors, they might not be effective in deducing the confidence interval regarding the mean value function. In such cases, software developers cannot estimate the possible risk variation in software reliability by using the randomness of the mean value function, thus reducing the decision‐making reliability when determining an optimal software release time. In this paper, the method of stochastic differential equations is used to build a software reliability model, which is validated based on practical data previously used in six published papers. Moreover, the estimation of the parameters of the proposed model, which can be defined as the autonomous error‐detected factor and the learning factor, is also illustrated, and the results of model validation empirically confirm that the proposed model is able to account for a fairly large portion of the variance of the mean value function. Additionally, the confidence intervals of the mean value function regarding software faults are employed to assist software developers in determining the optimal release times at different confidence levels. Finally, a numerical example is given to verify the effectiveness of the proposed model. Copyright © 2008 John Wiley & Sons, Ltd.     

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。     

基于排错等待延迟的广义动态集成神经网络模型

软件可靠性增长模型在可靠性评估与保障中具有重要作用,针对软件测试过程中的故障检测和排错等待延迟问题,提出了一种考虑故障排错等待延迟的广义动态集成神经网络模型(RWD-SRGM)。该模型考虑软件工程的多样性,利用神经网络方法构建广义动态集成模型,并考虑排错等待延迟现象完成故障检测和预测。通过2组真实失效数据集(DS1和DS2)的实验,将所提模型与现有的软件可靠性增长模型进行了比较,结果显示考虑故障排错等待延迟的神经网络模型拟合效果最优,表现出了更好的软件可靠性评估性能和模型通用性。     

Optimal stopping of multi-project software testing in the context of software cybernetics

Software cybernetics explores the interplay between control theory/engineering and software theory/engineering. The controlled Markov chains (CMC) approach to software testing follows the idea of software cybernetics and treats software testing as a control problem. The software under test serves as a controlled object and the software testing strategy serves as the corresponding controller. The software under test and the software testing strategy make up a closed-loop feedback control system, and the theory of controlled Markov chains can be used to design and optimize software testing strategies in accordance with testing/reliability goals given a priori. In this paper we apply the CMC approach to the optimal stopping problem of multi-project software testing. The problem under consideration assumes that a single stopping action can stop testing of all the software systems under test simultaneously. The theoretical results presented in this paper describe how to test multiple software systems and whe     

A Study of Uncertainty in Software Cost and Its Impact on Optimal Software Release Time

For a development software project, management often faces the dilemma of when to stop testing the software and release it for operation, which requires careful decision-making as it has great impact on both software reliability and project cost. In most existing research on optimal software release problem, the cost considered was the expected cost (EC) of the project. However, what management concerns is the actual cost (AC) of the project rather than the EC. Treatment (such as minimization) of the EC may not ensure a desired low level of the AC, due to the uncertainty (variability) involved in the AC. In this paper, we study the uncertainty in software cost and its impact on optimal software release time in detail. The uncertainty is quantified by the variance of the AC and several risk functions. A risk-control approach to optimal software release problem is proposed. New formulations of the problem which are extensions of current formulations are developed, and solution procedures are established. Several examples are presented. Results reveal that it seems crucial to take account of the uncertainty in software cost in optimal software release problem, otherwise unsafe decision may be reached which could be a false dawn to management.     

考虑故障相关的软件可靠性增长模型研究

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.     

开放网络环境下软件安全性测试技术研究

在开放网络环境下软件容易受到攻击,导致软件故障,需要进行安全性测试,针对无监督类测试方法开销较大和复杂度较高的问题,提出一种基于半监督自适应学习算法的软件安全性测试方法;首先采用模糊度量原理构建软件安全测试的半监督学习数学模型,分析软件产生安全性故障的数组特征,然后通过软件故障的熵特征分布方法进行软件的可靠性度量,在开放式网络环境下建立软件可靠性云决策模型,实现安全性测试和故障定位;最后通过仿真实验进行性能验证,结果表明,采用该方法进行软件安全性测试,对软件故障定位的准确度较高,测试的实时性较好,保障了软件的安全可靠运行。