用于软件开发工作量估算的IOP模型

软件开发工作量估算可以为多项与组织决策和项目管理相关的任务提供有效的支持.根据工作量估算的不同目标,通过对COCOMO Ⅱ成本驱动因子进行扩充和对国内外最新软件项目数据进行回归分析,建立了一个用于工作量估算的IOP模型.该模型采用统一框架,分别从行业水平、组织水平和项目特征3个层次实现基于规模的软件开发工作量估算,以满足针对软件行业、软件组织和特定软件项目的不同的估算目标,例如项目招标、软件组织不同项目的管理和具体软件项目的管理等.最后,给出了IOP模型应用的若干实例.     

Estimating Maintenance Effort by Analogy

Effort estimation is a key step of any software project. This paper presents a method to estimate project effort using an improved version of analogy. Unlike estimation methods based on case-based reasoning, our method makes use of two nearest neighbors of the target project for estimation. An additional refinement based on the relative location of the target project is then applied to generate the effort estimate. We first identify the relationships between cost drivers and project effort, and then determine the number of past project data that should be used in the estimation to provide the best result. Our method is then applied to a set of maintenance projects. Based on a comparison of the estimation results from our estimation method and those of other estimation methods, we conclude that our method can provide more accurate results.     

A Survey of Boehm's Work on the Spiral Models and COCOMO II—Towards Software Development Process Quality Improvement

Successful engineering and evaluation of complex software depends on successfully completing all the stages of the Software Development Life Cycle. There have been many models which illustrate the stages Software Engineers have to go through to produce software. This paper investigates one of these—The Spiral Model (Sommerville 1997) with particular reference to recent enhancements to it, examines the interaction of COCOMO II (19xx) within the WinWin framework, and reports on a case study using the WinWin Spiral Model (Boehm et al. 1998) to develop software.     

构造性成本模型COCOMO

软件估算已成为众多研究的主题。而构造性成本模型COCOMO无疑是其中最为浓墨重彩的一笔。COCO-MO以其独特的优势成为应用范围最广的软件成本估算模型,也是其他众多软件估算方法或模型研究对比、参考和引用的对象。所以对其进行全面而深入的分析是很有意义的。本文在剖析模型原理的同时,把COCOMO作为软件成本估算模型的优势、存在的问题及未来的发展之路作为本文讨论的重点。     

A controlled experiment in assessing and estimating software maintenance tasks

Context

Software maintenance is an important software engineering activity that has been reported to account for the majority of the software total cost. Thus, understanding the factors that influence the cost of software maintenance tasks helps maintainers to make informed decisions about their work.

Objective

This paper describes a controlled experiment of student programmers performing maintenance tasks on a C++ program. The objective of the study is to assess the maintenance size, effort, and effort distributions of three different maintenance types and to describe estimation models to predict the programmer’s effort spent on maintenance tasks.

Method

Twenty-three graduate students and a senior majoring in computer science participated in the experiment. Each student was asked to perform maintenance tasks required for one of the three task groups. The impact of different LOC metrics on maintenance effort was also evaluated by fitting the data collected into four estimation models.

Results

The results indicate that corrective maintenance is much less productive than enhancive and reductive maintenance and program comprehension activities require as much as 50% of the total effort in corrective maintenance. Moreover, the best software effort model can estimate the time of 79% of the programmers with the error of or less than 30%.

Conclusion

Our study suggests that the LOC added, modified, and deleted metrics are good predictors for estimating the cost of software maintenance. Effort estimation models for maintenance work may use the LOC added, modified, deleted metrics as the independent parameters instead of the simple sum of the three. Another implication is that reducing business rules of the software requires a sizable proportion of the software maintenance effort. Finally, the differences in effort distribution among the maintenance types suggest that assigning maintenance tasks properly is important to effectively and efficiently utilize human resources.     

软件开发成本估算模型的研究

软件成本准确地估算和管理控制是软件项目开发成功的基础。该文分析了影响软件开发成本估算准确性的因素,并详细介绍了IBM模型、Putnam模型和COCOMO模型三种经验估算模型,探讨了软件开发成本估算的新需求。     

基于COCOMOⅡ的自动测试维护代价实例研究

不考虑自动测试的维护代价,盲目进行自动化测试是有风险的。文中以画板程序升级为例,详细介绍了它的修改过程和相应测试程序的维护代价,利用COCOMOⅡ度量方法,对测试程序和被测试程序的维护代价做一个比较,说明测试程序和被测试程序一样,需要反复维护才能进行回归测试。通过实例和理论分析使测试者清醒认识自动测试的维护代价,合理利用自动化测试。     

Contrasting ideal and realistic conditions as a means to improve judgment-based software development effort estimation

Context

The effort estimates of software development work are on average too low. A possible reason for this tendency is that software developers, perhaps unconsciously, assume ideal conditions when they estimate the most likely use of effort. In this article, we propose and evaluate a two-step estimation process that may induce more awareness of the difference between idealistic and realistic conditions and as a consequence more realistic effort estimates. The proposed process differs from traditional judgment-based estimation processes in that it starts with an effort estimation that assumes ideal conditions before the most likely use of effort is estimated.

Objective

The objective of the paper is to examine the potential of the proposed method to induce more realism in the judgment-based estimates of work effort.

Method

Three experiments with software professionals as participants were completed. In all three experiments there was one group of participants which followed the proposed and another group which followed the traditional estimation process. In one of the experiments there was an additional group which started with a probabilistically defined estimate of minimum effort before estimating the most likely effort.

Results

We found, in all three experiments, that estimation of most likely effort seems to assume rather idealistic assumptions and that the use of the proposed process seems to yield more realistic effort estimates. In contrast, starting with an estimate of the minimum effort, rather than an estimate based on ideal conditions, did not have the same positive effect on the subsequent estimate of the most likely effort.

Conclusion

The empirical results from our studies together with similar results from other domains suggest that the proposed estimation process is promising for the improvement of the realism of software development effort estimates.     

On the application of cross correlation function to subsample discrete time delay estimation

Cross correlation function (CCF) of signals is an important tool of multi-sensors signal processing. Parabola functions are commonly used as parametric models of the CCF in time delay estimation. The parameters are determined by fitting samples near the maximum of the CCF to a parabola function. In this paper we analyze the CCF for the stationary processes of exponential auto-correlation function, with respect to two important types of sensor sampling kernels. Our analysis explains why the parabola is an acceptable model of CCF in estimating the time delay. More importantly, we demonstrate that the Gaussian function is a better and more robust approximation of CCF than the parabola. This new approximation approach leads to higher precision in time delay estimation using the CCF peak locating strategy. Simulations are also carried out to evaluate the performance of the proposed estimation method for different sample window sizes and signal to noise ratios. The new method offers significant improvement over the current parabola based method.     

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。     

基于实例的自动测试维护代价研究*

不考虑自动测试的维护代价,盲目进行自动化测试是有风险的。以画板程序升级为例,详细介绍了它的修改过程和相应测试程序的维护代价,然后利用COCOMO度量方法,对测试程序与被测试程序的维护代价作一个比较,说明测试程序和被测试程序一样,需要反复维护才能进行回归测试。通过实例和理论分析使测试者清醒认识自动测试的维护代价,合理利用自动化测试。     

The effects of request formats on judgment-based effort estimation

In this paper we study the effects of a change from the traditional request “How much effort is required to complete X?” to the alternative “How much can be completed in Y work-hours?”. Studies 1 and 2 report that software professionals receiving the alternative format provided much lower, and presumably more optimistic, effort estimates of the same software development work than those receiving the traditional format. Studies 3 and 4 suggest that the effect belongs to the family of anchoring effects. An implication of our results is that project managers and clients should avoid the alternative estimation request format.     

基于随机子空间的局部鉴别投影*

针对高维数据容易对噪声敏感及容易造成维数灾难问题,文中提出基于随机子空间的局部鉴别投影算法(RSLDP).利用随机子空间方法对高维的原始数据进行特征选择,在生成的低维特征子空间构造近邻图,降低噪声影响.RSLDP通过最大化局部类间加权散度和最小化局部类内加权散度,同时最小化样本的总体局部散度,改进局部最大间距鉴别嵌入算法,较好刻画样本与其类间类内近邻中心点的关系,有利于鉴别特征的提取.在CMU PIE和AR这2个人脸数据库上的实验表明文中算法的有效性.     

软件项目估算*

软件项目估算是软件项目管理的一个重要组成部分．本文所介绍的估算法是作者在外国软件公司工作期间的工作总结和经验归纳．