an empirical study on classification of non-functional requirements
文献类型：会议论文
| 作者 | Zhang Wen ; Yang Ye ; Wang Qing ; Shu Fengdi |
| 出版日期 | 2011 |
| 会议名称 | SEKE 2011 - Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering |
| 会议日期 | July 7, 20 |
| 会议地点 | Miami, FL, United states |
| 关键词 | Experiments Knowledge engineering Semantics Support vector machines |
| 页码 | 444-449 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | Knowledge Systems Institute Graduate School |
| 会议录 | SEKE 2011 - Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering
 |
| 语种 | 英语 |
| ISBN号 | 1891706292 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16268]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 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]. 见:SEKE 2011 - Proceedings of the 23rd International Conference on Software Engineering and Knowledge Engineering. Miami, FL, United states. July 7, 20. |
入库方式： OAI收割
来源：软件研究所
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