一种改进的软件项目开发风险管理模型

该文提出了一种改进的软件项目开发风险管理模型。该模型在贝叶斯网络的建模过程中以样本数据集为基础进行结构学习和参数学习,建立更符合实际软件项目特征的贝叶斯网络。同时,进一步完善了软件项目开发风险管理流程,并利用贝叶斯网络的信念更新过程实现动态软件项目风险管理。经实践检验,该改进模型能够更有效地对软件项目开发过程中的风险进行管理,提高软件开发的成功率。     

一种改进的软件项目投资风险评价模型

提出一种改进的软件项目投资风险评价模型。该模型在软件项目投资风险评价指标体系的基础上采用因子分析的方法进行降维处理,从而减少问题分析的维度。同时,在B-P神经网络的建模过程中利用一种基于黄金分割原理的优化算法确定隐含层节点数,提高了风险评价的精度。实际评价数据表明,该模型能够有效地完成软件项目投资风险的评价。     

基于EM-GA改进贝叶斯网络的研究及应用*

为了解决软件风险分析中可能出现的数据不完整以及影响因素间关系复杂的问题,提出了一种改进贝叶斯网络的软件项目风险分析方法。将遗传算法和EM算法相结合得到EM-GA算法,利用EM-GA算法对软件项目分析过程中贝叶斯网络结构中的参数进行学习,同时优化网络结构,通过实例验证了该方法的有效性及可行性。     

基于贝叶斯网络的软件项目进度管理模型

根据软件项目的特点以及软件项目进度的安排,本文提出了基于贝叶斯网络的软件项目进度管理模型,在PERT图的基础上构造贝叶斯网络模型,由专家判断和工程经验确定网络中的概率参数。该模型可实现对项目进展情况的监控和控制,识别开发中对项目影响的不确定性因素,并进行反向参数学习,从而可以及时地调整不合理的开发进度,以达到优化的作用。仿真实验结果表明,该模型与实际情况相符合,应用于实际项目开发中取得了很好的效果。     

贝叶斯网在软件项目管理中的应用

为了提高软件项目管理水平,针对软件项目过程的不确定性,利用其进展过程中层次关系所蕴含的条件独立性,提出了一种层次结构的贝叶斯推理网络模型并给出了相关的学习算法和推理步骤.该模型可以在专家给出状态间关联度的情况下,计算出条件概率.该模型揭示了项目状态间的关联关系,有助于项目管理中的风险分析和预测.最后通过一个具体事例,说明了该网络在项目状态预测和缺陷原因的界定的应用.     

基于贝叶斯网络的软件项目风险管理模型

提出了一种基于贝叶斯网络的软件项目风险管理模型。随着软件项目的进行,该风险管理模型能够利用不断更新的项目数据持续地预测潜在风险,确定风险源并采取适当的应对措施降低风险发生概率。经实践检验,在软件开发过程中引入该风险管理模型能够有效地对风险进行管理,提高软件开发的成功率。     

基于贝叶斯网络的软件项目风险分析过程

论文提出了一种基于贝叶斯网络的软件项目风险分析过程。随着软件项目的进行,该风险分析过程能够利用不断更新的项目数据持续地预测潜在风险,并以此确定风险源并采取适当的应对措施降低风险发生概率。经实践检验,在软件开发的风险分析过程中引入贝叶斯网络技术能够有效地对风险进行管理,提高软件开发的成功率。     

面向可信软件的风险评估方法

风险管理逐渐成为开发高质量软件过程中的重要的组成部分。风险评估作为风险管理的重要活动之一,是风险控制的前提。贝叶斯网络作为风险管理的有力工具之一,是处理不确定性的有效方法。结合贝叶斯网络与模糊理论,提出一种风险评估方法,首先使用贝叶斯网络对影响可信软件的风险因素进行风险概率评估,然后利用模糊综合评价法进行风险综合影响评估。该方法用于软件项目的风险评估,为开发高质量的可信软件提供新策略。     

贝叶斯分类器的判别式参数学习

为了提高贝叶斯分类器的分类性能,针对贝叶斯网络分类器的构成特征,提出一种基于参数集成的贝叶斯分类器判别式参数学习算法PEBNC。该算法将贝叶斯分类器的参数学习视为回归问题,将加法回归模型应用于贝叶斯网络分类器的参数学习,实现贝叶斯分类器的判别式参数学习。实验结果表明,在大多数实验数据上,PEBNC能够明显提高贝叶斯分类器的分类准确率。此外,与一般的贝叶斯集成分类器相比,PEBNC不必存储成员分类器的参数,空间复杂度大大降低。     

基于MapReduce的贝叶斯网络参数学习方法

期望最大化(Expectation Maximization,EM)算法常被应用于贝叶斯网络参数学习过程,但在处理海量数据时由于迭代计算过程的复杂性和处理器、内存等资源的限制,该算法的效能受到极大影响;通过对大数据环境下传统线性贝叶斯网络参数学习方法计算复杂性瓶颈问题的研究,提出了基于MapReduce平台的贝叶斯网络并行期望最大化(Parallel Expectation Maximization,PEM)参数学习算法;利用不完备训练样本集,对态势评估贝叶斯网络进行参数学习;仿真结果表明:在大数据条件下PEM算法能够准确的学习网络参数,同时有效减少参数学习所需时间且具有较好的可拓展性。     

A systematic literature review on the applications of Bayesian networks to predict software quality

Bayesian networks (BN) have been used for decision making in software engineering for many years. In other fields such as bioinformatics, BNs are rigorously evaluated in terms of the techniques that are used to build the network structure and to learn the parameters. We extend our prior mapping study to investigate the extent to which contextual and methodological details regarding BN construction are reported in the studies. We conduct a systematic literature review on the applications of BNs to predict software quality. We focus on more detailed questions regarding (1) dataset characteristics, (2) techniques used for parameter learning, (3) techniques used for structure learning, (4) use of tools, and (5) model validation techniques. Results on ten primary studies show that BNs are mostly built based on expert knowledge, i.e. structure and prior distributions are defined by experts, whereas authors benefit from BN tools and quantitative data to validate their models. In most of the papers, authors do not clearly explain their justification for choosing a specific technique, and they do not compare their proposed BNs with other machine learning approaches. There is also a lack of consensus on the performance measures to validate the proposed BNs. Compared to other domains, the use of BNs is still very limited and current publications do not report enough details to replicate the studies. We propose a framework that provides a set of guidelines for reporting the essential contextual and methodological details of BNs. We believe such a framework would be useful to replicate and extend the work on BNs.     

A Bayesian network framework for project cost,benefit and risk analysis with an agricultural development case study

Successful implementation of major projects requires careful management of uncertainty and risk. Yet such uncertainty is rarely effectively calculated when analysing project costs and benefits. This paper presents a Bayesian Network (BN) modelling framework to calculate the costs, benefits, and return on investment of a project over a specified time period, allowing for changing circumstances and trade-offs. The framework uses hybrid and dynamic BNs containing both discrete and continuous variables over multiple time stages. The BN framework calculates costs and benefits based on multiple causal factors including the effects of individual risk factors, budget deficits, and time value discounting, taking account of the parameter uncertainty of all continuous variables. The framework can serve as the basis for various project management assessments and is illustrated using a case study of an agricultural development project.     

The optimization of success probability for software projects using genetic algorithms

The software development process is usually affected by many risk factors that may cause the loss of control and failure, thus which need to be identified and mitigated by project managers. Software development companies are currently improving their process by adopting internationally accepted practices, with the aim of avoiding risks and demonstrating the quality of their work.This paper aims to develop a method to identify which risk factors are more influential in determining project outcome. This method must also propose a cost effective investment of project resources to improve the probability of project success.To achieve these aims, we use the probability of success relative to cost to calculate the efficiency of the probable project outcome. The definition of efficiency used in this paper was proposed by researchers in the field of education. We then use this efficiency as the fitness function in an optimization technique based on genetic algorithms. This method maximizes the success probability output of a prediction model relative to cost.The optimization method was tested with several software risk prediction models that have been developed based on the literature and using data from a survey which collected information from in-house and outsourced software development projects in the Chilean software industry. These models predict the probability of success of a project based on the activities undertaken by the project manager and development team. The results show that the proposed method is very useful to identify those activities needing greater allocation of resources, and which of these will have a higher impact on the projects success probability.Therefore using the measure of efficiency has allowed a modular approach to identify those activities in software development on which to focus the project's limited resources to improve its probability of success. The genetic algorithm and the measure of efficiency presented in this paper permit model independence, in both prediction of success and cost evaluation.     

Enhancing ontology-based antipattern detection using Bayesian networks

Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. The antipattern ontology has been recently proposed as a knowledge base for SPARSE, an intelligent system that can detect the antipatterns that exist in a software project. However, apart from the plethora of antipatterns that are inherently informal and imprecise, the information used in the antipattern ontology itself is many times imprecise or vaguely defined. For example, the certainty in which a cause, symptom or consequence of an antipattern exists in a software project. Taking into account probabilistic information would yield more realistic, intelligent and effective ontology-based applications to support the technology of antipatterns. However, ontologies are not capable of representing uncertainty and the effective detection of antipatterns taking into account the uncertainty that exists in software project antipatterns still remains an open issue. Bayesian Networks (BNs) have been previously used in order to measure, illustrate and handle antipattern uncertainty in mathematical terms. In this paper, we explore the ways in which the antipattern ontology can be enhanced using Bayesian networks in order to reinforce the existing ontology-based detection process. This approach allows software developers to quantify the existence of an antipattern using Bayesian networks, based on probabilistic knowledge contained in the antipattern ontology regarding relationships of antipatterns through their causes, symptoms and consequences. The framework is exemplified using a Bayesian network model of 13 antipattern attributes, which is constructed using BNTab, a plug-in developed for the Protege ontology editor that generates BNs based on ontological information.     

基于生命周期理论的软件项目投标风险评估

传统软件项目投标的风险评估往往局限在投标过程的某个阶段,且评估具有较强的主观性。针对此问题,本文基于项目生命周期理论,采用熵权系数法和AHP方法来确定各个风险因素和项目生命周期各阶段风险的相对权值,利用模糊综合评判法对软件项目投标的风险进行综合评估。实例分析表明：所建立的风险评估模型克服了主观判断的弊端,使投标者明确整个项目生命周期及生命周期各阶段的风险控制的重点领域,具有良好的适用性。     

基于可拓集的软件工程安全监理的研究

针对软件工程的信息安全监理中各风险因子间的关联性及评价因素难以精确度量的问题,将可拓集方法与软件工程中信息风险因子结合,建立风险评估模型并实现基于可拓集的安全监理方法.基于该监理模型,可拓集方法将评价因素的定性表达区间化并利用区间关联函数实现定性定量的转化,从而实现风险的定性与定量相结合的评估,达到更好的监理效果.     

基于BP神经网络的软件项目风险评估研究*

为了确定软件开发项目中不确定因素的影响,提出基于BP神经网络的软件项目风险评估模型。首先,构建了软件项目风险识别的TEMP（technology、environment、management、process）模型;其次,在TEMP识别模型基础上建立了包括17种风险指标在内的软件项目风险评估指标体系;再次,利用BP神经网络建立了风险评估模型;最后,通过MATLAB实例证明该风险评估模型的有效性和可行性。