软件项目策划中的工作量估算方法探讨

文章对软件项目策划中工作量的估算方法进行了探讨,并重点介绍了一种基于组织经验和项目的实际情况来确定估算系数并进行工作量估算的方法,旨在为软件企业进行工作量估算、实行过程改进提供参考和帮助。     

一种基于WebMO模型的迭代式软件估算方法

改进了基于WebMO模型的软件规模和工作量估算方法，通过调整项目的计数规范、修正基于Web的规模预测因子、增加软件配置模块等途径，对实际的Web项目进行了迭代式估算。应用结果表明，改进方法在对Web软件项目的估算可靠性方面有了一定的提高。     

跨组织数据的软件成本估算方法

软件成本估算一直是软件项目管理的重要部分。经过半个多世纪的研究和工业实践,成本估算方法、模型得到了极大的丰富。这些方法、模型也衍生出了各种成本估算工具。但是,成本估算方法和模型的基础是历史项目数据。没有历史项目数据的公司和组织只能利用其他公司或组织的数据来进行自己项目的成本估算。如何利用跨组织数据进行有效的成本估算成为更具现实意义的问题。针对这一问题,提出了一种有效利用跨组织数据进行成本估算的方法,并通过实验说明了方法的有效性。     

浅谈软件项目估算技术

由于软件产品自身的特殊性,导致软件项目的估算工作进行困难,估算结果准确性差。为了解决这一问题,产生了很多不同的软件项目估算技术,本文对各种估算技术的主要思想及其优缺点进行简单的阐述。尤其是对功能点估算技术,本文做了详细的介绍,并通过实例加以说明其应用方法。     

软件成本估算研究

软件成本估算是对将要开发的或正在开发的软件项目所需要的工作量和工作进度作出预测,从而产生出一组在可接受误差范围内的近似规划。该文着重论述了软件成本估算的内容,以及软件成本估算的方法。     

一种软件开发成本估算方法

针对软件成本估算困难的问题,提出一种基于灰色理论的估算方法。给出了设计思想、关键技术以及估算的过程,并进行了实验验证。该方法主要借鉴灰色理论可以基于小样本和贫信息的特性,结合GM（1,1）和Verhulst各自的优点,利用成本序列及其变化率动态构造灰色理论模型进行软件成本估算。实验结果表明,该方法平均估算误差比线性回归方法低10%~50%。     

面向过程的软件项目估算

面向过程的软件项目估算方法,提供了一种方法框架支持实践中最流行的估算技术一专家估算和类似估算,目的是使估算透明和可重复,它基于软件开发过程的标准化分解,RUP用作开发过程的一个例子,估算是基于项目中执行的活动。     

软件项目维护成本估算模型研究

针对软件维护的工作量估算,根据统计学原理,给出估算的模型和模型的抽取方法。首先介绍通过变量相关性分析,进行变量的筛选方法,然后提出"分步递进"和"分步递减"模型提取算法。该算法充分融合了专家经验和数学工具。在对模型的评价上,除传统的误差均值、误差方差的指标外,还介绍了通过误差分布情况对模型进行分析和改进的方法。最后通过一个具体实例说明了模型提取全过程。     

一种基于WebMO模型的迭代式软件估算方法

改进了基于WebMO模型的软件规模和工作量估算方法,通过调整项目的计数规范、修正基于Web的规模预测因子、增加软件配置模块等途径,对实际的Web项目进行了迭代式估算.应用结果表明,改进方法在对Web软件项目的估算可靠性方面有了一定的提高.     

一种软件工作量估算的不确定性度量方法

以COCOMO81模型为基础,结合模型输出方差以及模型与数据自身方差的组合,分别度量模型本身估算值与实际值差距的不确定性,并在此基础上给出预测区间。提出以命中率(SR)与平均相对宽度(MRV)相结合的评测标准。通过采用重采样获得N对训练和测试集合,计算不同置信度下区间SR和MRV的均值,运用SR和MRV的散点图比较不同度量方法获得区间。实验结果表明,该方法能以相同的命中率获得更窄的区间。     

Cost estimation for cross-organizational ERP projects: research perspectives

There are many methods for estimating size, effort, schedule and other cost aspects of IS projects, but only one specifically developed for Enterprise Resource Planning (ERP) (Stensrud, Info Soft Technol 43(7):413–423, 2001) and none for simultaneous, interdependent ERP projects in a cross-organizational context. The objective of this paper is to sketch the problem domain of cross-organizational ERP cost estimation, to survey available solutions, and to propose a research program to improve those solutions. In it, we: (i) explain why knowledge in the cost estimation of cross-organizational ERP is fragmented, (ii) assess the need to integrate research perspectives, and (iii) propose research directions that an integrated view of estimating cross-organizational ERP project cost should include.

An Examination of Determinants of Software Testing and Project Management Effort

Software estimation research has primarily focused on software effort involved in direct software development. As more and more organizations buy instead of building software, more effort is spent on software testing and project management. In this empirical study, the effect of program duration, computer platform, and software development tool (SDT) on program testing effort and project management effort is studied. The study results point to program duration and software tool as significant determinants of testing and management effort. Computer platform, however, does not have an effect on testing and management effort. Furthermore, the mean testing effort for third generation (3G) development environment was significantly higher than the mean testing effort for fourth generation (4G) environments that used IDE. In addition, the management effort for 4G environment projects without the use of IDE was lower than nonprogramming report generation projects.     

基于属性相关度的Web数据库大小估算方法

提出了一种基于词频统计的方法以估算Web数据库的规模.通过分析Web数据库查询接口中属性之间的相关度来获取某个属性上的一组随机样本;并对该属性分别提交由前k位高频词形成的试探查询以估算Web数据库中记录的总数.通过在几个真实的Web数据库上进行实验验证,说明该方法可以准确地估算出Web数据库的大小.     

一种软件开发过程的工作量评估模型

提出一个评估模型用以估算软件生产过程的工作量。模型依据用户故事的规模及复杂度来预估工作量,根据团队开发能力以及开发过程可能遇到的多种负面和突发性因素计算开发速度,结合工作量和开发速度计算得到预计的开发时间,方便软件项目管理者在项目初期准确的估计完成时间,以便提高生产率以及在行业中的信誉度。     

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.     

On Building Prediction Systems for Software Engineers

Building and evaluating predictionsystems is an important activity for software engineering researchers.Increasing numbers of techniques and datasets are now being madeavailable. Unfortunately systematic comparison is hindered bythe use of different accuracy indicators and evaluation processes.We argue that these indicators are statistics that describe propertiesof the estimation errors or residuals and that the sensible choiceof indicator is largely governed by the goals of the estimator.For this reason it may be helpful for researchers to providea range of indicators. We also argue that it is useful to formallytest for significant differences between competing predictionsystems and note that where only a few cases are available thiscan be problematic, in other words the research instrument mayhave insufficient power. We demonstrate that this is the casefor a well known empirical study of cost models. Simulation,however, could be one means of overcoming this difficulty.     

Estimating the long-term cost to serve new customers in joint distribution

One of the most important concerns for logistics service providers is to identify the distribution cost to serve each new customer for pricing. Compared to the analysis through cost allocation on delivery routes, cost estimation possesses the advantage of robust costing rules but is a very challenging problem due to the complex collaborative mechanisms of distribution. Based on the activities leading to a distribution cost, we analyze the relationship between multiple geographic factors and cost, and then construct appropriate attributes for estimation. Combining a data selection approach and regression or artificial neural network techniques, a prediction scheme is proposed to build models, and an explicit continuous approximation model is suggested for efficient implementation. Computational experiments demonstrate the importance of the constructed attributes and the accuracy of the proposed cost estimation method. The impacts from cost stability and delivery frequency are examined to provide further explanation and support for practical implementation.     

基于AADL语言的移动软件能耗评估方法

移动软件往往部署在电量受限的处理器上,能耗已成为评价这类软件的一个重要质量属性.与代码级和指令级相比,在设计级进行能耗评估具有耗时短、成本低的优点,近年来已成为软件工程学术界和工业界的研究热点.目前虽已涌现出一些设计级能耗评估方法,但这些方法大多未对软件构件的内部行为元素进行能耗评估,导致了精度问题.针对上述问题,基于体系结构分析设计语言AADL和StrongARM处理器构建了一种移动软件能耗评估模型,进一步定义了面向AADL语言的移动软件能耗评估过程,在此基础上研发了一款能耗评估工具,进而提出一种基于AADL语言的移动软件能耗评估方法.实验结果表明该方法较已有AADL能耗评估方法在精度上有所提高.     

Managing Software Development Projects for Maximum Productivity

In the area of software development, data processing management often focuses more on coding techniques and system architecture than on how to manage the development. In recent years, ``structured programming' and ``structured analysis' have received more attention than the techniques software managers employ to manage. Moreover, these coding and architectural considerations are often advanced as the key to a smooth running, well managed project. This paper documents a philosophy for software development and the tools used to support it. Those management techniques deal with quantifying such abstract terms as ``productivity,' ``performance,' and ``progress,' and with measuring these quantities and applying management controls to maximize them. The paper also documents the application of these techniques on a major software development effort.     

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task. However, finding the best estimation results in software development is challenging. Thus, accurate estimation of software development efforts is always a concern for many companies. In this paper, we proposed a novel software development effort estimation model based both on constructive cost model II (COCOMO II) and the artificial neural network (ANN). An artificial neural network enhances the COCOMO model, and the value of the baseline effort constant A is calibrated to use it in the proposed model equation. Three state-of-the-art publicly available datasets are used for experiments. The backpropagation feedforward procedure used a training set by iteratively processing and training a neural network. The proposed model is tested on the test set. The estimated effort is compared with the actual effort value. Experimental results show that the effort estimated by the proposed model is very close to the real effort, thus enhanced the reliability and improving the software effort estimation accuracy.