基于加权案例推理模型族的软件成本SVR组合估算  

Combination Estimation of Software Effort by Support Vector Regression Based on Multiple Case-Based Reasoning with Optimized Weight

作　　者：吴登生[1] 李建平[1] 孙晓蕾[1] 

机构地区：[1]中国科学院科技政策与管理科学研究所,北京100190

出　　处：《管理工程学报》2015年第2期210-216,共7页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(71201156;91218302;70531040);中国科学院青年促进会基金资助项目

摘　　要：精确地估算软件成本是软件项目成功开发的一个重要保证,直接影响着软件的风险控制和质量保证。为了更好地解决单一估算模型的不足,提出了集成多案例推理(CBR)模型的软件成本组合估算模型。首先,采用六种距离计算公式刻画新旧项目相似度,构建了六种CBR模型,并运用粒子群算法(PSO)来优化CBR模型族中的属性权重。其次,在CBR模型族的基础上,运用支持向量回归机(SVR)模型将不同CBR模型的估算结果进行集成,提高软件成本估算结果的精度。利用Desharnais数据库对模型有效性进行检验,实证结果表明,在六种CBR模型中Euc-CBR、Min-CBR、Gau-CBR和Mah-CBR模型估算结果没有明显差异,Gre-CBR和Man-CBR模型略优;提出的SVR组合估算模型估算精度明显优于单CBR模型和其他线性组合估算模型,能有效提高软件成本的估算精度。As a significant part of software process, software effort estimation plays a central role in controlling software cost, reducing software risk and guaranteeing software quality. The software development process is knowledge-intensive and under a dynamic development environment, which will increase the difficulty of solcware effort estimation. Therefore, software effort estimation is one of the most challenging activities in software development process. According to the idea of model integration and combination forecasting, the study puts forward the support vector regression based combination of multiple case-based reasoning to estimate software effort, in order to overcome the shortage of single estimation model and improve the estimation accuracy. The basic case-based reasoning （CBR） method for software effort estimation, including Similarity measure, Weight optimization, Number of most similar projects and project adaptation, Evaluation criterion, is introduced. Furthermore, six independent CBR methods, derived from Euclidean distance （Euc-CBR）, Manhattan distance （Man-CBR）, Minkowski distance （Min-CBR）, Grey Relational Coefficient （Gre-CBR）, Gaussian distance （Gau-CBR） and Mahalanobis distance （Mah-CBR） are constructed for software effort estimation based on the literature review. Moreover, particle swarm optimization （PSO） is adopted to determine suitable weights for each attribute due to its strong search capability. The CBR methods proposed in the study identify the estimation relationship between effort and other attribute from different aspects, and get a different estimation result, which will generates a gap between the estimation results. For this reason, the study applies the support vector regression （SVR） to combine the multiple CBR estimation results in order to integrate the estimated knowledge and improve estimation accuracy. Based on statistical learning theory, SVR have good generalization ability considering the structural risk minimization principle. T

关 键 词：软件成本估算 基于案例推理 组合预测 支持向量回归机 粒子群算法 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]