主页期刊介绍编委会编辑部服务介绍道德声明在线审稿编委办公编辑办公English
2018-2019年专刊出版计划 微信服务介绍 最新一期:2018年第8期
     
在线出版
各期目录
纸质出版
分辑系列
论文检索
论文排行
综述文章
专刊文章
美文分享
各期封面
E-mail Alerts
RSS
旧版入口
中国科学院软件研究所
  
投稿指南 问题解答 下载区 收费标准 在线投稿
连小利,张莉.面向软件产品线中特征选择的多目标优化算法.软件学报,2017,28(10):2548-2563
面向软件产品线中特征选择的多目标优化算法
Multi-Objective Optimization Algorithm for Feature Selection in Software Product Lines
投稿时间:2015-07-13  修订日期:2015-11-18
DOI:10.13328/j.cnki.jos.005130
中文关键词:  软件产品线  特征选择  多目标优化算法  非功能需求  功能需求
英文关键词:software product line  feature selection  multi-objective optimization algorithm  non-functional requirement  functional requirement
基金项目:国家自然科学基金(61370058)
作者单位E-mail
连小利 北京航空航天大学 计算机学院, 北京 100191  
张莉 北京航空航天大学 计算机学院, 北京 100191 lily@buaa.edu.cn 
摘要点击次数: 1078
全文下载次数: 598
中文摘要:
      软件产品线中,产品定制的核心是选择合适的特征集.由于多个非功能需求间往往相互制约甚至发生冲突,特征选择的本质是多目标优化过程.优化过程的搜索空间被特征间错综复杂的依赖和约束关系以及明确的功能需求大大限制.另外,有些非功能需求有明确的数值约束,而有些则仅要求尽可能地得到优化.多样的非功能需求约束类型也给优化选择过程带来极大的挑战.提出一种含修正算子的多目标优化算法MOOFs.首先,设计特征间依赖和约束关系描述语言DL-DCF来统一规范特征选择过程中必须遵守的规则,所有的非功能需求都转化为优化目标,相关的数值约束则作为优化过程中特征选择方案的过滤器.另外,设计了修正算子用于保证选择出的特征配置方案必满足产品线的特征规则约束.通过与4种常用的多目标优化算法在4个不同规模的特征模型上的运行结果进行对比,表明该方法能够更快地产生满足约束的优化解,且优化解具备更好的收敛性与多样性.
英文摘要:
      In software product lines, the core of product customization is to select appropriate features.Due to the various competing and even conflicting non-functional requirements (NFRs), feature selection, in essential, is a multi-objective optimization process.What's more, the search space in optimization is constrained largely by the relationships between features and the definitive functional requirements (FRs).Besides, some NFRs are with clear numerical limits, while others are not.These varied types of NFRs also present challenges for feature selection.To solve these problems, a novel multi-objective optimization algorithm with a feature selection reviser is proposed.Firstly, description language for the dependency and constraints relationships between features (DL-DCF) are designed to format different types of relationships between features uniformly, which stipulates the coexistence of two or more features.Next, during selection, all NFRs are transformed to optimization goals, and the quantified constraints on NFRs are used as filters to exclude invalid solutions.Furthermore, a reviser is designed to repair the configuration which violates any relation between features or FRs.Finally, the reviser is planted into the multi-objective optimization framework to form the proposed algorithm, MOOFs, to perform feature selection.Comparing with four popular baselines running on four feature models with different scales, empirical results show notable performance improvement of the algorithm on efficiency of valid solution generation and on the multiple NFRs balancing, especially when the feature models are large and complex.
HTML  下载PDF全文  查看/发表评论  下载PDF阅读器
 

京公网安备 11040202500064号

主办单位:中国科学院软件研究所 中国计算机学会
编辑部电话:+86-10-62562563 E-mail: jos@iscas.ac.cn
Copyright 中国科学院软件研究所《软件学报》版权所有 All Rights Reserved
本刊全文数据库版权所有,未经许可,不得转载,本刊保留追究法律责任的权利