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。     

基于COCOMO模型的软件成本估算工具研究

软件成本估算在软件项目管理中占据重要地位,目前已有多种软件成本估算的模型．其中COCOMO模型最为人们所关注。但对于如何估算该模型中的软件规模这一问题．相关的研究还较少。若能从功能点分析方法入手,计算出软件的功能点数目。再将它根据统计结果转换成相应的软件规模,将会大大提高COCOMO模型使用的准确程度．并进一步提高COCOMO工具的实用性。     

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。     

A Procedure for Assessing the Influence of Problem Domain on Effort Estimation Consistency

By and large, given the inherent subjectivity in defining and measuring factors used in algorithmic effort estimation methods, when algorithmic methods produce consistent estimates it seems reasonable to assume that this is in part due to estimator experience. Further, software development factors are usually assumed to have different degrees of influence on actual effort. For example, no specific allowances for program language or problem domain were made in the original COCOMO model or in Albrecht's Function Points, whilst allowances for development mode in COCOMO and function type complexity for Albrecht's Function Points are crucial. However, work has been conducted that concluded that 4GLs are associated with higher productivity than 3GLs. Clearly, we can support such conclusions about productivity, since, for example, it usually requires less effort to develop a database using a purposely designed DBMS product than it does using a 3GL. However, in general, for a given problem domain an appropriate development language and platform will be selected. Hence, we might feel that an appropriate development language will not be a factor that influences estimate consistency unduly, given that an estimator has experience of the problem domain. However, algorithmic methods usually require calibration to different problem domains. Calibration may be needed because the method was originally designed using data from another type of domain. Furthermore, estimators' estimation consistency within problem domains may be affected for one or more reasons. Intuitively, reasons might include: estimators lack estimation experience in some domains; or the development team(s) may have different levels of experience in different domains, which the estimator finds difficult to take into account. We demonstrate how, in general, the influence of problem domain may be assessed using a Hierarchical Bayesian inference procedure. We also show how values can be derived to account for variations in estimate consistency in problem domains.     

Analyzing and handling local bias for calibrating parametric cost estimation models

ContextParametric cost estimation models need to be continuously calibrated and improved to assure more accurate software estimates and reflect changing software development contexts. Local calibration by tuning a subset of model parameters is a frequent practice when software organizations adopt parametric estimation models to increase model usability and accuracy. However, there is a lack of understanding about the cumulative effects of such local calibration practices on the evolution of general parametric models over time.ObjectiveThis study aims at quantitatively analyzing and effectively handling local bias associated with historical cross-company data, thus improves the usability of cross-company datasets for calibrating and maintaining parametric estimation models.MethodWe design and conduct three empirical studies to measure, analyze and address local bias in cross-company dataset, including: (1) defining a method for measuring the local bias associated with individual organization data subset in the overall dataset; (2) analyzing the impacts of local bias on the performance of an estimation model; (3) proposing a weighted sampling approach to handle local bias. The studies are conducted on the latest COCOMO II calibration dataset.ResultsOur results show that the local bias largely exists in cross company dataset, and the local bias negatively impacts the performance of parametric model. The local bias based weighted sampling technique helps reduce negative impacts of local bias on model performance.ConclusionLocal bias in cross-company data does harm model calibration and adds noisy factors to model maintenance. The proposed local bias measure offers a means to quantify degree of local bias associated with a cross-company dataset, and assess its influence on parametric model performance. The local bias based weighted sampling technique can be applied to trade-off and mitigate potential risk of significant local bias, which limits the usability of cross-company data for general parametric model calibration and maintenance.     

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。     

软件测试成本评估方法研究

测试是软件领域的一项重要技术,软件测试成本评估是软件项目成本评估的重要组成部分,一直以来软件测试成本评估比较难实现。本文涉及的软件测试成本评估主要分为3个步骤：首先评估软件规模;其次根据不同的软件规模运用不同的评估模型评估出软件生存周期总工作量,模型包括适用于小型软件的基于经验法则的评估模型,适用于中型软件的COCOMO模型,适用于大型软件的动态多变量模型;最后则是软件测试成本的评估。     

工作量估算模型在软件开发平台上的应用

针对基于企业内部开发平台的软件项目开发,选取COCOMO Ⅱ模型中合理的比例因子和成本驱动因子,提出对COCOMO Ⅱ模型的常数A进行校准的方法,使软件开发工作量的估算更加准确,并提取在企业内部软件开发平台上完成的5组项目数据进行举例说明。     

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。