软件开发项目风险模型分析

随着产业的发展和软件规模的提高,软件项目开发和使用过程中超支、延时、技术缺陷等现象越来越严重。如何在项目实施的过程中进行有效地评估和预防这些风险,以达到识别和消除不利因素对软件开发的影响,部属于软件项目风险管理问题．该文着重讨论了集中软件开发项目风险模型并对其进行了相关分析。     

软件项目风险管理研究

进行大型的软件项目开发过程中,由于种种难以预见的不确定性因素,会给软件项目带来造成失败的风险.首先介绍了软件风险的基本概念,分析了软件项目风险的特点,以及如何识别软件风险.对项目管理中的风险分析、风险管理进行了详细地探讨,提出对风险进行量化管理.最后对软件开发过程中各阶段的风险进行了分析和研究,给出了减轻或防止风险发生的几种方法.研究软件项目风险管理对于软件开发、软件项目管理及软件产品质量都具有重要意义.     

软件项目风险管理综述

风险在软件开发过程中无处不在,如何控制软件项目中出现的各种风险一直是软件工程领域研究的主要问题之一软件开发过程方法对控制软件风险有积极的作用,RUP、CMM等方法对软件项目风险有着良好的控制能力很多学者都使用不同的方法或技术对软件项目风险进行分析或管理。     

软件项目风险管理理论和方法探究

随着网络时代到来,软件开发技术有了飞跃式发展。然而,软件项目开发过程中存在诸多风险因素,而风险管理却经常被忽视,从某种意义而言,这制约了软件项目发展。近几年,人们已意识到软件项目风险管理的重要性,并加大了对理论和方法的研究力度,并取得了进步。作者认为软件项目风险管理为软件工程的关键部分,对其理论和方法进行研究具有现实意义。该文对软件项目风险管理概念进行简析,并对其理论和方法展开探究。     

灰色聚类法在软件项目风险评价中的应用

软件风险的控制在当今软件开发过程中显得越来越重要,而软件项目风险评价的好坏直接影响到风险的管理。目前,大多数软件项目风险评估技术本质上都是基于主观的,而客观地依赖于度量技术进行软件项目风险分析的研究还很少。针对软件项目风险管理的要求,建立了评价指标体系,提出了基于灰色聚类的软件项目风险评价方法。文中案例分析证明了该方法的科学性、客观性与合理性。     

浅析软件开发项目过程中的风险管理

软件项目开发中存在的各类风险会影响项目计划的实现,如果项目风险变成现实,就有可能影响项目的进度,增加项目的成本,甚至使软件项目不能实现。成功的项目管理一般都对项目风险进行了良好的管理。该文就软件开发项目过程中存在的风险进行分析,并比较了几种主流的风险管理模型,探讨如何对项目进行有效的风险管理,从而最大限度的减少风险的发生。     

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。     

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。     

一种基于贝叶斯网络的软件项目风险管理方法

提出一种新的软件项目风险管理方法,采用贝叶斯网络同时对风险发生概率和风险影响进行推理.该风险管理方法能够随着软件项目的进行持续地评估潜在风险,并采取适当的措施应对风险.实践证明,在软件开发过程中引入该风险管理方法能够有效地对风险进行管理,提高软件开发的成功率.     

贝叶斯网络在软件项目风险评估中的应用

在软件项目生存周期早期或创新型项目的研发过程中,可用的案例数据很少或很不完整,项目风险多由专家经验进行主观评估,给风险的客观度量带来了很大的困难。提出了一种基于贝叶斯网络的软件项目风险评估方法,不仅可度量风险影响程度的风险当量,还能度量出多种风险对某种风险后果的组合影响以及单个风险对整体后果的综合影响,从而增强了软件项目风险的预测和应变能力,为有效地降低风险发生概率、提高软件开发成功率提供了一种新的途径。     

软件项目风险管理的研究

Under the background of information age, the demand of enterprises and individuals for software and related equip- ment is increasing. The software and its affiliated projects have a lot of complexity and uncertainty in the development process. Therefore, risk management for software projects is crucial. This paper firstly expounds the importance of software project risk man- agement, then analyzes the software project risk identification, evaluation and specific countermeasures.     

Quality Approaches in a Large Software House

Many methods, techniques, standards and tools exist for controlling software development, but large, complex projects may face extra problems due to continuous, rapid changes during the project life cycle. Such changes must be tracked, analysed, and reconciled to ensure high quality in the end-product; otherwise problems may be lost or ignored in the overall complexity. A large software company needs to develop effective procedures to control their whole organisation's projects; for example to control more than 100 different types of projects at the same time is a big challenge for most large software companies. This paper is a case study on how a large company benefits from using quality assurance processes and quality standards. It provides details of the process engineering system running in Kent Ridge Digital Labs (KRDL). Most of project managers and top directors in KRDL believe that Good quality software will save millions of dollars! This paper also highlights the benefits of KRDL being able to handle more than 100 projects at the same time and still provide high quality software.     

基于成熟度的开源软件立项评估方法

随着智能移动设备和社交媒体的发展,出现了越来越多面向最终用户的应用软件.如何充分了解最终用户的需求,从而减少软件项目的风险,成为亟待解决的问题.在各类软件项目中,开源软件项目的开发具有特殊性,体现在参与人员的广泛性、多层次性,以及用户需求的多面性和不稳定性等方面.在项目成熟度评估的基础上,提出了开源软件项目的立项评估模型,并详细介绍了成熟度评估的方法及基于成熟度的开源软件项目立项评估流程,通过一个项目实例分析了在该模型基础上的立项评估过程.     

A statistical analysis of the effects of Scrum and Kanban on software development projects

Traditionally, software development processes have relied on the use of the “Waterfall” and “Vee” models. Later, Agile methodologies were used to handle the challenges of managing complex projects during the development phase. Agile methodologies are a group of incremental and iterative methods that are more effective, and have been used in project management. Kanban and Scrum are two powerful Agile project management approaches in software development. The objective of Scrum and Kanban is achieved by optimizing the development process by identifying the tasks, managing time more effectively, and setting-up teams. A review of the literature reveals that there is a lack of statistical evidence to conclude which methodology is more effective in dealing with the traditional project management factors of budget handling, risk control, quality of the project, available resources, having clear project scope, and schedule handling. This research statistically compares the effectiveness of the Scrum and Kanban methods in terms of their effects on the project management factors for software development projects. Numerical analysis is performed based on survey responses from those with experience in the Scrum and Kanban methods. Results suggest that both Scrum and Kanban lead to the development of successful projects, and that the Kanban method can be better than the Scrum method in terms of managing project schedule.     

AHP与FUZZY在软件开发项目风险管理中的综合应用

归纳了软件开发项目的几大常见风险,在此基础上,通过集成层次分析法AHP和模糊综合评判法FUZZY的原理和模型,设计出软件开发项目风险评价的指标体系,并运用Delphi方法,让多名专家对各项指标赋权而得出每个指标的权重,之后通过风险因素U到模糊评判评语集V的模糊映射,得到模糊评估矩阵。最后根据FUZZY理论,运用模糊矩阵的合成运算,对软件开发项目风险进行两级评估,从而构建了集成方法下软件开发项目风险评价的过程和模型。结论对软件开发项目管理者具有实际参考价值。     

An empirical analysis of the impact of software development problem factors on software maintainability

Many problem factors in the software development phase affect the maintainability of the delivered software systems. Therefore, understanding software development problem factors can help in not only reducing the incidence of project failure but can also ensure software maintainability. This study focuses on those software development problem factors which may possibly affect software maintainability. Twenty-five problem factors were classified into five dimensions; a questionnaire was designed and 137 software projects were surveyed. A K-means cluster analysis was performed to classify the projects into three groups of low, medium and high maintainability projects. For projects which had a higher level of severity of problem factors, the influence on software maintainability becomes more obvious. The influence of software process improvement (SPI) on project problems and the associated software maintainability was also examined in this study. Results suggest that SPI can help reduce the level of severity of the documentation quality and process management problems, and is only likely to enhance software maintainability to a medium level. Finally, the top 10 list of higher-severity software development problem factors was identified, and implications were discussed.     

An Empirical Approach to Characterizing Risky Software Projects Based on Logistic Regression Analysis

During software development, projects often experience risky situations. If projects fail to detect such risks, they may exhibit confused behavior. In this paper, we propose a new scheme for characterization of the level of confusion exhibited by projects based on an empirical questionnaire. First, we designed a questionnaire from five project viewpoints, requirements, estimates, planning, team organization, and project management activities. Each of these viewpoints was assessed using questions in which experience and knowledge of software risks are determined. Secondly, we classify projects into “confused” and “not confused,” using the resulting metrics data. We thirdly analyzed the relationship between responses to the questionnaire and the degree of confusion of the projects using logistic regression analysis and constructing a model to characterize confused projects. The experimental result used actual project data shows that 28 projects out of 32 were characterized correctly. As a result, we concluded that the characterization of confused projects was successful. Furthermore, we applied the constructed model to data from other projects in order to detect risky projects. The result of the application of this concept showed that 7 out of 8 projects were classified correctly. Therefore, we concluded that the proposed scheme is also applicable to the detection of risky projects.     

Exploring the relationship between software project duration and risk exposure: A cluster analysis

Software projects often fail. Thus it is important to find ways to ensure a successful outcome. One significant area is a better understanding of the relationship between the software project duration and risk exposure, as this helps project managers with pertinent information to be effective in managing risky projects. We addressed this need by adopting a cluster analysis technique to provide managers with insight into effective planning and control of their projects. The results not only revealed that risk exposures associated with user, requirement, planning & control and team risk dimensions were affected by project duration, but also showed how to manage software risks effectively through observing trends in the risk components. Based on our findings, project managers can adopt appropriate attitudes, skills, and practices to deal with risky areas more effectively rather than just identifying those software risks with which project managers should be concerned.