协同过滤在软件成本估算中的应用  

Application in Cost Estimation of Collaborate Filtering

作　　者：任雪利[1] 

机构地区：[1]曲靖师范学院计算机科学与工程学院,曲靖655011

出　　处：《计算机系统应用》2014年第6期246-249,共4页Computer Systems & Applications

基　　金：云南省教育厅基金(2011Y010)

摘　　要：准确的成本估算是软件项目管理的重要目标,但是现有的成本估算方法均有缺点.协同过滤是一种群体智慧的方法,已成功的应用于电子商务、影视推荐等多个领域.本文将协同过滤技术应用于软件项目中成本的估算,由于传统的协同过滤技术仅能处理数值型数据,而描述项目特征的属性既有数值型也有非数值型数据,因此采用不同的策略对属性进行归一化,使用均值对缺失值进行处理,余弦相似度用于计算项目间的相似度,确定近邻集进行成本估算.将该方法应用于USP05-FT数据集,实验结果表明:估算结果的准确性可以达到80%以上.Accurate project cost prediction is an important goal for the software engineering community, but there are some defects in the method to estimate software cost. Collaborative Filtering has been developed in information retrieval researchers successfully which recommends items based on other user＇s reference in historical data set. Cost estimation based on Collaborative Filtering is researched. Because only numerical data can be handled in traditional collaborative filtering technology, and there are non-numeric numeric data in the attributes that describe the project characteristics, so the different strategies are used to normalize for describing the project＇s properties. And then the mean values are used for the missing contents. Cosine similarity is used to calculate the similarity between projects. Finnally cost is estimated using the weighted sum of the efforts in k-nearest neighbors. The method is applied in an experimental case to evaluate the effort estimation, and the result shows the accuracy of estimation may arrive to 80%.

关 键 词：协同过滤 成本 类比 归一化 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论] TP391.3[自动化与计算机技术—计算机科学与技术]