项目度量中对基本COCOMO进行校准的研究

所有成功的软件组织都将度量作为保证自己管理和技术质量的重要手段,软件成本估计则是软件度量[1,2]的核心任务。为了提高成本估算的准确性,文中根据特定软件企业中的历史项目数据对基本COCOMO模型进行校准,在具体的参数修正方法上利用对数数据相关算法进行校正,并与其它方法进行了比较,得到了满意的结果。校准后的模型对项目开发成本的预测将会更加准确,从而切实体现COCOMO成本度量工作对于软件项目的指导价值。因此,文中所做的成本估算模型的校准工作,对软件开发企业非常具有实用价值。     

项目度量中对基本COCOMO进行校准的研究

所有成功的软件组织都将度量作为保证自己管理和技术质量的重要手段，软件成本估计则是软件度量的核心任务。为了提高成本估算的准确性，文中根据特定软件企业中的历史项目数据对基本COCOMO模型进行校准，在具体的参数修正方法上利用对数数据相关算法进行校正，并与其它方法进行了比较，得到了满意的结果。校准后的模型对项目开发成本的预测将会更加准确，从而切实体现COCOMO成本度量工作对于软件项目的指导价值。因此，文中所做的成本估算模型的校准工作，对软件开发企业非常具有实用价值。     

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.     

一种面向ERP外包项目的开发成本估算方法

对于软件项目而言,项目成本的有效控制是每个项目取得成功的标志之一。恰当的软件开发成本估算方法将大大提高成本估算的稳定性和可靠性,从而提高项目经理对项目成本的有效控制。本文在深入分析目前业界常用的软件项目开发成本估算方法的基础上,针对ERP外包软件项目开发生命周期的特点,提出了以ERP程序单元为最小单位的一种项目开发成本估算法,即FRICE估算法。该估算方法已经在大量ERP外包软件项目中得到了成功应用、实践和验证,它能有效地帮助项目经理对项目开发成本进行估算、控制和管理。     

基于专家经验和随机抽样的早期快速估算方法

项目立项阶段由于评审时间有限,需要采用快速近似估算方法获取软件的规模以确定经费预算。本文提出基于专家经验和随机抽样的快速估算方法,将软件分解为不同的软件模块,由开发组织对不同的模块规模和成本进行预估并申报经费,估算人员利用专家经验以及对软件组件随机抽样进行详细度量,再利用快速近似估算方法估算出软件项目的整体规模,进而得到软件整体成本。实际应用表明,该方法是可行的。     

基于类比的软件成本估算及其一种改进方法

基于类比的软件成本估算方法利用相似的历史项目信息来预测目标项目的属性值,具有不需要校准、能够在没有统计关系可以利用的情况下发挥作用等优点。本文在类比估算四个基本步骤的框架下,详细说明了类比估算所必须进行的相关操作,并参照案例推理系统把类比估算过程描述为一个从数据收集、属性选取开始,到估算报告输出、结果重用的循环结构。根据估算实践,提出了针对大数据集的计算优化方法,不仅有效降低了估算过程中的计算量,而且在一定程度上避免了次要因素的影响,提高了估算的可信度。     

基于决策树的软件项目估算方法

软件工程领域的一个重要问题是预测软件开发项目的规模、工作量和成本,即软件项目估算问题。基于机器学习的方法在软件项目估算领域具有优势地位,本文提出了基于决策树的聚类分析预测方法,通过对目标项目的目标属性进行正确分类,预测目标属性的取值范围。通过对502个ISBSG v9项目数据集中的项目进行基于C4.5算法的分类预测,正确率达到82.4701%,满足了软件项目估算的指标要求。     

RUP估算过程模型

软件行业估算追踪记录显示软件项目的失败率仍很高,估算问题是基本的原因之一.估算方法的创新没有出现期望的突破,而通过可控的过程,可以获得期望的结果.提出了一个过程模型,用于指导软件项目展开一系列估算相关的活动.该过程模型包括两部分,一是RUP估算过程,其详细描述了RUP开发模型里每个开发管理阶段应如何进行估算;二是用贝叶斯网络对RUP估算过程模型建立图形化推理模型,它能有效地用于估算分析、交流、权衡以及风险预测等.RUP估算过程解决了估算活动的定义问题,但不便于形成清晰的估算视图.软件估算的特点很适合用贝叶斯网络进行建模.贝叶斯工作量估算模型是RUP估算过程模型的抽象;ESFQ模型详细建模了软件项目关键因素之间的权衡关系.案例分析证明了该过程模型的适用性.     

基于CBR的软件项目成本估算方法

针对软件项目前期成本估算不准确问题,通过构建软件项目案例库,提出一种基于CBR的软件项目成本估算方法(CBRCEM)。根据COCOMO模型成本驱动因子理论,确定影响项目成本属性特征;引入归一化效用函数,应用层次分析法计算影响项目成本属性的权重值;通过对常用案例检索算法的比较分析,结合软件成本估算的特性,建立基于改进的灰色关联分析理论的软件项目相似度算法;根据PERT理论估算软件项目成本,使估算结果更为准确。CBRCEM在Windows平台上用JAVA语言开发完成并在实际中加以应用,案例研究结果表明,对于软件项目前期成本估算,该方法能够得到更加准确的评估结果。     

基于加权类比的软件成本估算方法

软件成本估算是软件项目开发周期、管理决策和软件项目质量中最重要的问题之一。针对软件研发成本估算在软件行业中普遍存在不准确、难以估算的问题,提出一种基于加权类比的软件成本估算方法,将相似度距离定义为具有相关性的马氏距离,通过优化的粒子群算法优化后得到权值,并用类比法估算软件成本。实验结果表明,该方法 具有 比非加权类比、神经网络等非计算模型方法更高的精确度。实际案例测试表明,该方法在软件开发初期基于需求分析的软件成本估算比专家估算有更精确的评估结果。     

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.     

Cross versus Within-Company Cost Estimation Studies: A Systematic Review

The objective of this paper is to determine under what circumstances individual organizations would be able to rely on cross-company-based estimation models. We performed a systematic review of studies that compared predictions from cross-company models with predictions from within-company models based on analysis of project data. Ten papers compared cross-company and within-company estimation models; however, only seven presented independent results. Of those seven, three found that cross-company models were not significantly different from within-company models, and four found that cross-company models were significantly worse than within-company models. Experimental procedures used by the studies differed making it impossible to undertake formal meta-analysis of the results. The main trend distinguishing study results was that studies with small within-company data sets (i.e., $20 projects) that used leave-one-out cross validation all found that the within-company model was significantly different (better) from the cross-company model. The results of this review are inconclusive. It is clear that some organizations would be ill-served by cross-company models whereas others would benefit. Further studies are needed, but they must be independent (i.e., based on different data bases or at least different single company data sets) and should address specific hypotheses concerning the conditions that would favor cross-company or within-company models. In addition, experimenters need to standardize their experimental procedures to enable formal meta-analysis, and recommendations are made in Section 3.     

基于模型的软件成本估计方法

准确的估计是进行有效的项目计划、跟踪和控制的基础.基于模型的成本估计方法是软件成本估计研究的重点,它可分为算法驱动式模型、数据驱动式模型以及复合式模型.依照该分类模式,介绍了典型的软件成本估计方法,并从内部属性及外部评价两个维度共计11个指标对每类方法的假设前提、适用范围、优势及局限性进行深入的分析.最后,对软件成本估计研究的未来发展进行探讨.     

A Simulation Tool for Efficient Analogy Based Cost Estimation

Estimation of a software project effort, based on project analogies, is a promising method in the area of software cost estimation. Projects in a historical database, that are analogous (similar) to the project under examination, are detected, and their effort data are used to produce estimates. As in all software cost estimation approaches, important decisions must be made regarding certain parameters, in order to calibrate with local data and obtain reliable estimates. In this paper, we present a statistical simulation tool, namely the bootstrap method, which helps the user in tuning the analogy approach before application to real projects. This is an essential step of the method, because if inappropriate values for the parameters are selected in the first place, the estimate will be inevitably wrong. Additionally, we show how measures of accuracy and in particular, confidence intervals, may be computed for the analogy-based estimates, using the bootstrap method with different assumptions about the population distribution of the data set. Estimate confidence intervals are necessary in order to assess point estimate accuracy and assist risk analysis and project planning. Examples of bootstrap confidence intervals and a comparison with regression models are presented on well-known cost data sets.     

Segmented software cost estimation models based on fuzzy clustering

Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees.This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart.     

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.     

Cross-company vs. single-company web effort models using the Tukutuku database: An extended study

In 2004 [Kitchenham, B.A., Mendes, E., 2004a. Software productivity measurement using multiple size measures. IEEE Transactions on Software Engineering 30 (12), 1023-1035, Kitchenham, B.A., Mendes, E., 2004b. A comparison of cross-company and single-company effort estimation models for web applications. In: Proceedings Evaluation and Assessment in Software Engineering (EASE’ 04), pp. 47-55] (S1) investigated, using data on 63 Web projects, to what extent a cross-company cost model could be successfully employed to estimate development effort for single-company Web projects. Their effort models were built using Forward Stepwise Regression (SWR) and they found that cross-company predictions were significantly worse than single-company predictions. This study S1 was extended by Mendes and Kitchenham [Mendes, E., Kitchenham, B.A., 2004. Further comparison of cross-company and within company effort estimation models for web applications. In: Proceedings International Software Metrics Symposium (METRICS’04), Chicago, Illinois, September 11-17th, 2004. IEEE Computer Society, pp. 348-357] (S2), who used SWR and Case-based reasoning (CBR), and data on 67 Web projects from the Tukutuku database. They built two cross-company and one single-company models and found that both SWR cross-company models and CBR cross-company data provided predictions significantly worse than single-company predictions. Since 2004 another 83 projects were volunteered to the Tukutuku database, and recently used by Mendes et al. [Mendes, E., Di Martino, S., Ferrucci, F., Gravino, C., in press. Effort estimation: How valuable is it for a web company to use a cross-company data set, compared to using its own single-company data set? In: Proceedings of International World Wide Web Conference (WWW’07), Banff, Canada, 8-12 May] (S3), who partially replicated Mendes and Kitchenham’s study (S2), using SWR and CBR. They corroborated some of S2’s findings (SWR cross-company model and the CBR cross-company data provided predictions significantly worse than single-company predictions) however they replicated only part of S2. The objective of this paper (S4) is therefore to extend Mendes et al.’s work and fully replicate S2. We used the same dataset used in S3, and our results corroborated most of those obtained in S2. The main difference between S2 and our study was that one of our SWR cross-company models showed significantly similar predictions to the single-company model, which contradicts the findings from S2.     

Transfer learning for cross-company software defect prediction

Context

Software defect prediction studies usually built models using within-company data, but very few focused on the prediction models trained with cross-company data. It is difficult to employ these models which are built on the within-company data in practice, because of the lack of these local data repositories. Recently, transfer learning has attracted more and more attention for building classifier in target domain using the data from related source domain. It is very useful in cases when distributions of training and test instances differ, but is it appropriate for cross-company software defect prediction?

Objective

In this paper, we consider the cross-company defect prediction scenario where source and target data are drawn from different companies. In order to harness cross company data, we try to exploit the transfer learning method to build faster and highly effective prediction model.

Method

Unlike the prior works selecting training data which are similar from the test data, we proposed a novel algorithm called Transfer Naive Bayes (TNB), by using the information of all the proper features in training data. Our solution estimates the distribution of the test data, and transfers cross-company data information into the weights of the training data. On these weighted data, the defect prediction model is built.

Results

This article presents a theoretical analysis for the comparative methods, and shows the experiment results on the data sets from different organizations. It indicates that TNB is more accurate in terms of AUC (The area under the receiver operating characteristic curve), within less runtime than the state of the art methods.

Conclusion

It is concluded that when there are too few local training data to train good classifiers, the useful knowledge from different-distribution training data on feature level may help. We are optimistic that our transfer learning method can guide optimal resource allocation strategies, which may reduce software testing cost and increase effectiveness of software testing process.     

A study of project selection and feature weighting for analogy based software cost estimation

A number of software cost estimation methods have been presented in literature over the past decades. Analogy based estimation (ABE), which is essentially a case based reasoning (CBR) approach, is one of the most popular techniques. In order to improve the performance of ABE, many previous studies proposed effective approaches to optimize the weights of the project features (feature weighting) in its similarity function. However, ABE is still criticized for the low prediction accuracy, the large memory requirement, and the expensive computation cost. To alleviate these drawbacks, in this paper we propose the project selection technique for ABE (PSABE) which reduces the whole project base into a small subset that consist only of representative projects. Moreover, PSABE is combined with the feature weighting to form FWPSABE for a further improvement of ABE. The proposed methods are validated on four datasets (two real-world sets and two artificial sets) and compared with conventional ABE, feature weighted ABE (FWABE), and machine learning methods. The promising results indicate that project selection technique could significantly improve analogy based models for software cost estimation.