一种基于特征模型和协同过滤的需求获取方法  

A Requirements Elicitation Approach Based on Feature Model and Collaborative Filtering

作　　者：彭珍连[1,2] 王健[1] 何克清[1] 唐明董[2] 

机构地区：[1]软件工程国家重点实验室(武汉大学),武汉430072 [2]湖南科技大学计算机科学与工程学院,湖南湘潭411201

出　　处：《计算机研究与发展》2016年第9期2055-2066,共12页Journal of Computer Research and Development

基　　金：国家"九七三"重点基础研究发展计划基金项目(2014CB340404);国家自然科学基金项目(61373037;61202031;61572186;61562073)~~

摘　　要：随着互联网和Web服务相关技术的快速发展,基于互联网进行软件开发越来越受到软件开发从业者的青睐.软件开发是一种多知识密集型过程,其中需求获取对软件系统的成功具有关键作用.基于互联网的软件需要满足大量地理位置各异、类型不同的客户需求,这增加了需求获取的难度;与此同时,互联网上相似类型的软件众多,这些具有大量相似功能的软件为软件需求获取提供了新的途径.为此,已有研究将推荐系统引入到软件需求获取过程中,借助于已有相似软件需求描述,为新软件推荐合适的缺失特征.为了提高推荐系统在软件需求预测和辅助获取过程中的准确率,提出了FM_KNN算法,利用特征模型中的特征类型以及特征间的关联关系,结合KNN(K-nearest neighbors)协同过滤推荐系统进行辅助需求获取.通过在真实数据集和仿真数据集上的实验和分析,验证了所提方法在预测准确率上具有更好的效果,从而为需求获取提供更好的支持.With the rapid development of Internet and software system on Internet is increasingly popular. Web service related technologies, developing Software development is a multi-knowledge- intensive process in which requirements elicitation plays a key role. Software systems deployed on Internet need to meet the needs of various kinds of customers and users who are geographically distributed,which increases the difficulties of software requirements elicitation. Meanwhile,more and more software systems that share similar functions are deployed on Internet, which provides a new way to elicit software requirements. Toward this end, recommender systems have been [everaged in the requirements elicitation to recommend missing features for software products by comparing the requirements descriptions of existing similar software systems. In order to improve the prediction accuracy of the recommended features of the software system, a requirements elicitation approach based on feature model and KNN （K-nearest neighbors） collaborative filtering recommendation system is proposed in this paper. An algorithm named FM_KNN is presented by utilizing constraint relations between features in KNN collaborative filtering recommendation system. Experiments conducted on a real data set and a simulated data set, by comparing the proposed FM_KNN with two existing methods, i. e. , KNN and an approach that combines association rule mining with KNN, show that the proposed approach can achieve higher precision.

关 键 词：需求获取 特征模型 协同过滤 推荐系统 特征推荐 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]