Earticle

초록

영어

Identification of analysis classes is a critical design decision to be made early in the design phase in software development. Although incorrect identification of analysis classes can diminish the quality of a whole software design, it still heavily relies on the expertise and experience of the developer and has been ad-hoc. The majority existing works on identification of analysis classes focus on the rule-based approaches. However, the rule-based approaches which are used for analyzing sentence structures cannot be adopted for the language, which has very flexible word order like Korean. In this paper, we proposed a statistical learning method for identification of analysis classes from requirements sentences in Korean. The approach is evaluated using the precision and recall of the automatically extracted candidate classes from real requirements sentences in Korean. The result shows that we can promise numerically measurable enhancement of performance on solving the automatic identification problem of analysis classes using statistical methods, in the real use case specifications of a banking system.

목차

Abstract
 1. Introduction
 2. Related Work
 3. B-I-O Tags and CRFs Classifier for Phrase Chunking
 4. Process for Identification of Analysis Classes
  4.1 Annotating Corpus
  4.2 Learning
  4.3 Extracting and Testing
 5. Discussion and Conclusion
 Acknowledgements
 References

키워드

저자

• Hyoungil Jeong [ Department of Computer Science and Engineering ]
• Jungyun Seo [ Department of Computer Science and Engineering, Interdisciplinary Program of Integrated Biotechnology ]
• Soojin Park [ Sogang Institute of Advanced Technology, Sogang University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605