JM模型的统计推断

本文对软件可靠性的著名模型-JM模型进行了研究,给出了这个模型中参数的最大似然估计存在的充要条件,并给出了软件可靠性的精确置信下限及其计算方法。     

软件可靠性模型的补偿算法

世界上已发表了四十多个软件可靠性模型,但使用情况不尽如人意．提出了模型的补偿算法,实验研究结果表明,该算法能有效地改善模型的精度及适应性．     

基于Markov的模块化电传操纵系统软件可靠性模型

电传操纵系统是影响飞行安全的关键系统,建立有效的电传操纵系统软件可靠性模型是软件可靠性测试和验证的基础.针对某型飞机系统安全性设计中横向通道电传操纵系统软件可靠性建模问题,建立了模块化的基于Markov的电传操纵系统软件可靠性模型.提出了综合考虑重要度和复杂度的软件可靠性指标分配方法,将可靠性目标分配到子模块.最后,通过实例验证了所建立的模型和指标分配方法的有效性.     

软件项目风险管理方法的比较与分析

本首先指出了传统的软件管理方法的不足，论述了开展软件项目风险管理的必要性。然后，从基本理论、前沿研究、实践应用、辅助工具和工作标准几个方面，综述了国内外取得的研究成果，继而就2002年来笔收集到的献资料，从成果形式(专、技术报告和论)和内容(一般理论、基本模型、辨识方法、评估方法、辅助工具、经验总结和其它)两方面进行了比较分析，最后结合我国当前的社会和技术环境，提出了应采取树立企业风险化、共享国外成果、建设风险信息数据库和“软”、“硬”结合的四项建议。     

考虑多种类型缺陷的软件可靠性模型研究

本文提出一种考虑多种类型缺陷的软件可靠性模型,并构建了缺陷检测和剔除两个过程的模型。具体分类情况,可以根据模型的检验方法(拟合准则和预测有效性度量)和模型复杂度来具体决定,如果有测试人员的分类建议或者分类数据,可以结合模型共同决定。为了说明问题,本文给出四种类型缺陷的具体模型,并对实际数据集进行了拟合。通过模型比较,验证了多种类型缺陷模型的有效性。最后,通过构建软件最优发布时间策略对模型进行了应用。研究结果为软件开发和测试提供了理论参考。     

软件可靠性模型和估计

本文根据何国伟提出的软件可靠性模型,特别是输入空间和输入分布的概念,分析软件测试数据特征,给出软件可靠性的点估计和置信下界的计算公式。     

软件可靠性M-O模型和逆线性模型的参数估计

Musa-Okumoto模型和逆线性模型是研究软件可靠性的重要模型，给出了在分组数据下，M－O模型和逆线性模型中参数的最大似然估计及其存在的充分性条件，指出了[4]中的错误，并且给出了一个实例。     

一种新的基于不完全排错的最优软件发行策略

软件的最优发行管理问题是软件可靠性研究的一个关键问题.现有的最优软件发行模型大都假定软件排错过程是完全的,并且在排错过程中没有新的故障引入,这种假设在很多情况下是不合理的.本文提出了一种新的最优软件发行管理模型,该模型既考虑了软件的不完全排错过程,又考虑了在排错过程中可能会引入新的故障,同时还考虑了由于排错经验的不断积累,软件的完全排错概率会增加的情况.本文同时给出了该模型的解.     

基于多属性群决策的软件可靠性模型选择方案

基于模型评价准则的属性值和专家经验的偏好信息进行软件可靠性增长模型的选择,可归结为多属性群决策问题.模型评价准则的主观专家偏好可利用层次分析方法将其量化得到其主观排序权重;基于失效数据的模型评价准则的计算数据可利用熵权法获得客观排序权重.综合两种权重可得到既能反映专家经验又能综合实际软件可靠性测试数据的模型评价方案.实例分析表明该方案具有合理性和可行性.     

基于一种新模型对软件可靠度的估计

本文通过修改JM模型给出了一种新的软件可靠性模型,并对软件可靠度给出了点估计和置信限.对实际数据的分析表明这种新模型的预测能力比JM模型要好.     

软件模块测试中的动态资源分配问题

为有效利用测度过程中投入的测试资源,提高测试效率,本文考虑将测试过程为多个阶段并为各个阶段动态分配测试资源的方法,为此提出两种动态分配测试资源模型,当测试资源总数一定时,测试约束时各软件模块中剩余错误平均数量小的模型;当给定测试约束时各软件模块中剩余错误平均数要达到的预定指标时,所用的测试资源最少的模型。     

Skill-based framework for optimal software project selection and resource allocation

This paper presents a conceptual framework and a mathematical formulation for software resource allocation and project selection at the level of software skills. First, we introduce a skill-based framework that considers universities, software companies, and potential projects of a country. Based on this framework, we formulate a linear integer program PMax which determines the selection of projects and the allocation of human resources that maximize profit for a certain company. We show that PMax is NP-complete. Therefore, we devise a meta-heuristic, called Tabu Select and Greedily Allocate (TSGA), to overcome the computational complexities. When compared to PMax running on CPLEX, TSGA performs 15 times faster with an accuracy of 98% on small to large size problems where CPLEX converges. On larger problems where CPLEX does not return an answer, TSGA computes a feasible solution in the order of minutes.     

Flexible software reliability growth model with testing effort dependent learning process

A lot of importance has been attached to the testing phase of the Software Development Life Cycle (SDLC). It is during this phase it is checked whether the software product meets user requirements or not. Any discrepancies that are identified are removed. But testing needs to be monitored to increase its effectiveness. Software Reliability Growth Models (SRGMs) that specify mathematical relationships between the failure phenomenon and time have proved useful. SRGMs that include factors that affect failure process are more realistic and useful. Software fault detection and removal during the testing phase of SDLC depend on how testing resources (test cases, manpower and time) are used and also on previously identified faults. With this motivation a Non-Homogeneous Poisson Process (NHPP) based SRGM is proposed in this paper which is flexible enough to describe various software failure/reliability curves. Both testing efforts and time dependent fault detection rate (FDR) are considered for software reliability modeling. The time lag between fault identification and removal has also been depicted. The applicability of our model is shown by validating it on software failure data sets obtained from different real software development projects. The comparisons with established models in terms of goodness of fit, the Akaike Information Criterion (AIC), Mean of Squared Errors (MSE), etc. have been presented.     

Statistical software reliability prediction and its applicability based on mean time between failures

One of the most important issues for a development manager may be how to predict the reliability of a software system at an arbitrary testing time. In this paper, using the software failure-occurrence time data, we discuss a method of software reliability prediction based on software reliability growth models described by an NHPP (nonhomogeneous Poisson process). From the applied software reliability growth models, the conditional probability distribution of the time between software failures is derived, and its mean and median are obtained as reliability prediction measures. Finally, based on several numerical examples, we compare the performance between these measures from the view point of software reliability prediction in the testing phase.     

Practical stopping criteria for validating safety-critical software by estimating impartial reliability

In this research, we investigate stopping rules for software testing and propose two stopping rules from the aspect of software reliability testing based on the impartial reliability model. The impartial reliability difference (IRD-MP) rule considers the difference between the impartial transition-probability reliabilities estimated for both software developer and consumers at their predetermined prior information levels. The empirical–impartial reliability difference (EIRD-MP) rule suggests stopping a software test when the computed empirical transition reliability is tending to its estimated impartial transition reliability. To insure the high-standard requirement for safety-critical software, both rules take the maximum probability (MP) of untested paths into account.     

Optimal allocation policies for testing-resource based on a software reliability growth model

A lot of development resources are consumed during the software testing phase fundamentally consisting of module testing, integration, testing and system testing. Then, it is of great importance for a manager to decide how to effectively spend testing-resources on software testing for developing a quality and reliable software.In this paper, we consider two kinds of software testing-resource allocation problems to make the best use of the specified testing-resources during module testing. Also, we introduce a software reliability growth model for describing the time-dependent behavior of detected software faults and testing-resource expenditures spent during the testing, which is based on a nonhomogeneous Poisson process. It is shown that the optimal allocation of testing-resources among software modules can improve software reliability.     

概率统计教学的几点建议

概率统计是大学数学的基础课程之一,我们通过具体的例子分析了概率统计课程教学应该重视的几个问题,并提出了解决问题的建议．这对概率统计课程的教学有一定的借鉴意义．     

利用循环展开最大化软件流水线性能

软件流水线通过重叠连续的循环实体来实现有效的精细调度．然而,其性能可能受限制于循环里缺乏足够的并行操作或者资源需求．‘‘先展开后调度”技术在进行软件流水线调度之前先展开循环,从而能够发现更多的并行操作和充分利用关键资源．研究循环展开如何影响软件流水线的性能和资源利用,并进一步提出如何选择优化的循环展开次数．     

On a labeling problem in graphs

Motivated by applications in software programming, we consider the problem of covering a graph by a feasible labeling. Given an undirected graph G=(V,E), two positive integers k and t, and an alphabet Σ, a feasible labeling is defined as an assignment of a set L_v⊆Σ to each vertex v∈V, such that (i) |L_v|≤k for all v∈V and (ii) each label α∈Σ is used no more than t times. An edge e={i,j} is said to be covered by a feasible labeling if L_i∩L_j≠0?. G is said to be covered if there exists a feasible labeling that covers each edge e∈E.In general, we show that the problem of deciding whether or not a tree can be covered is strongly NP-complete. For k=2, t=3, we characterize the trees that can be covered and provide a linear time algorithm for solving the decision problem. For fixed t, we present a strongly polynomial algorithm that solves the decision problem; if a tree can be covered, then a corresponding feasible labeling can be obtained in time polynomial in k and the size of the tree. For general graphs, we give a strongly polynomial algorithm to resolve the covering problem for k=2, t=3.     

Expert systems as a tool for statistics: A review

The current state of using expert system (ES) techniques for statistics is expounded through the difficulties of defining the notion of statistical expert system (SES) and through several well-known examples. The main characteristics of statistical expertise, and particularly the important concept of strategy are emphasized. The current state of SESs is seen in the context of the global trend of the evolution of ES, i.e. within the growth of second generation expert system (SGESs). The knowledge base of these ESs represent an attempt to represent not only experts' knowledge but also the way experts acquire their expertise, thanks to the existence of an underlying model. We show through many examples how far SESs are and/or will be able to be SGESs.