an empirical study on classification of non-functional requirements

ISCAS OpenIR

	an empirical study on classification of non-functional requirements
	Zhang Wen; Yang Ye; Wang Qing; Shu Fengdi
	2011
会议名称	SEKE 2011 - Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering
会议录名称	SEKE 2011 - Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering
页码	444-449
会议日期	July 7, 20
会议地点	Miami, FL, United states
收录类别	EI
ISBN	1891706292
部门归属	(1) Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China
摘要	The classification of NKRs brings about the benefits that NKRs with respect to the same type In the system can be considered and Implemented aggregately by developers, and as a result be verified by quality assurers assigned for the type. This paper conducts an empirical study on using text mining techniques to classify NFRs automatically. Three kinds of Index terms, which are at different levels of llngulstlcal semantics, as Vgrams. Individual words, and multi-word expressions (MWE), are used In representation of NFRs. Then. SVM (Support Vector Machine) with linear kernel bt used as the classifier. We collected a data set from PROMISE web site for experimentation In this empirical study. The experiments show that Index term as Individual words with Boolean weighting outperforms the other two Index terms. When MWEs are used to enhance representation of Individual words, there Is no significant Improvement on classification performance. Automatic classification produces better performance on categories of large stees than that on categories of small sizes. It can be drawn from the experimental results that for automatic classification of NFRs. Individual words are the best Index terms In text representation of short NFRs' description and we should collect as many as possible NFRs of software system.; The classification of NKRs brings about the benefits that NKRs with respect to the same type In the system can be considered and Implemented aggregately by developers, and as a result be verified by quality assurers assigned for the type. This paper conducts an empirical study on using text mining techniques to classify NFRs automatically. Three kinds of Index terms, which are at different levels of llngulstlcal semantics, as Vgrams. Individual words, and multi-word expressions (MWE), are used In representation of NFRs. Then. SVM (Support Vector Machine) with linear kernel bt used as the classifier. We collected a data set from PROMISE web site for experimentation In this empirical study. The experiments show that Index term as Individual words with Boolean weighting outperforms the other two Index terms. When MWEs are used to enhance representation of Individual words, there Is no significant Improvement on classification performance. Automatic classification produces better performance on categories of large stees than that on categories of small sizes. It can be drawn from the experimental results that for automatic classification of NFRs. Individual words are the best Index terms In text representation of short NFRs' description and we should collect as many as possible NFRs of software system.
关键词	Experiments Knowledge Engineering Semantics Support Vector Machines
主办者	Knowledge Systems Institute Graduate School
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16268
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhang Wen,Yang Ye,Wang Qing,et al. an empirical study on classification of non-functional requirements[C],2011:444-449.