Extended Application Research on Software Cost Estimation Model COCOMO II

Abstract

如何进行精确可靠的成本估算一直以来都是软件工程学术界和企业界普遍关注的问题。COCOMO II模型（Constructive Cost Model II）是目前最优秀和著名的软件成本估算模型之一，在过去的几十年里，吸引了大量的研究者对其进行研究和扩展，同时在工业界也得到了广泛的认可和应用。 针对COCOMO II模型在具体应用中的局限性，本文对COCOMO II模型进行了两个方面的扩展 。 其一，通过对COCOMO II模型的成本驱动因子按照三个级别进行分类和扩充，将COCOMO II由单级模型扩展为多级IOP模型。该模型采用统一框架，分别从行业(Industry)水平、组织(Organization)水平和项目(Project)特征三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 。 其二，通过对COCOMO II模型描述项目特征的成本驱动因子进行剪裁，将COCOMO II由通用模型扩展为应用于软件采购环境的特定模型。我们首先提出了一个用于软件采购定价的两阶段的成本估算模型框架。该框架使用扩展后的COCOMO II模型估算开发总工作量，再利用一个贝叶斯网络模型估算开发的财务总成本。以某类科技计划项目为例，对贝叶斯网络模型的网络结构和节点参数进行了定义和校准，将框架实例化为一个成本估算模型，并开发了以模型为估算内核的基于B/S结构的成本估算系统。 为了提高扩展后模型的有效性，我们收集了最新的国内外软件项目数据用于模型的参数确定和校准。同时，利用部分数据对模型进行了案例分析。实验结果表明，扩展后的模型拥有较高的估算精度。How to estimate effort and cost accurately is always a main focus for software research and industry groups. COCOMO II (Constructive Cost Model II) is one of the best and most famous software cost estimation models, which, in the past few years, attracted many researchers and was widely accepted and used in industry. Based on the limitation of COCOMO II application, we extend COCOMO II in two aspects. First of all, we extend COCOMO II from a single level model to a multi-level IOP model by classifying and expanding its cost drivers in three categories as Industry, Organization and Project. IOP-Model adopts a uniform framework and describes effort-and-size-relationship on these three levels. Its result in each level could explain problems of different hierarchy which software organizations and users focus on. Secondly, we extend COCOMO II from general use model to model used in software acquisition environment by pruning its cost drivers which describe project characteristics. First, we establish a two phase cost estimation model framework used for software acquisition pricing. The framework includes extended COCOMO II model for estimating developing effort and a Bayesian Network for estimating total cost. Then, taking some technology planning projects for example, we define and validate the network structure and node parameters of the Bayesian model to implement the framework to an estimation model. Based on that, we develop a B/S structure cost estimation system. In order to improve the model validity, we collect the latest domestic and international software project data for model parameter determination and calibration. We also use some data for case studies of the two extended model. The results show that the extended models have high estimation accuracy