Implementation of an Efficient Requirements Analysis supporting System using Similarity Measure Techniques

유사도 측정 기법을 이용한 효율적인 요구 분석 지원 시스템의 구현

  • Published : 2000.01.15

Abstract

As software becomes more complicated and large-scaled, user's demands become more varied and his expectation levels about software products are raised. Therefore it is very important that a software engineer analyzes user's requirements precisely and applies it effectively in the development step. This paper presents a requirements analysis system that reduces and revises errors of requirements specifications analysis effectively. As this system measures the similarity among requirements documents and sentences, it assists users in analyzing the dependency among requirements specifications and finding the traceability, redundancy, inconsistency and incompleteness among requirements sentences. It also extracts sentences that contain ambiguous words. Indexing method for the similarity measurement combines sliding window model and dependency structure model. This method can complement each model's weeknesses. This paper verifies the efficiency of similarity measure techniques through experiments and presents a proccess of the requirements specifications analysis using the embodied system.

소프트웨어가 점점 복잡해지고 대형화됨에 따라서 사용자의 요구가 매우 다양해지고 있으며, 제품에 대한 기대 수준도 높아지고 있다. 그러므로, 사용자의 요구 사항을 정확히 분석하여 효과적으로 개발 단계에 적용하는 것은 매우 중요하다. 본 논문에서는 자연어로 표현되는 요구 사항 문서의 분석 시에 나타나는 오류를 효과적으로 줄이고, 수정하는데 사용될 수 있는 요구 분석 시스템을 제안한다. 제안된 시스템은 문서간 유사도 측정에 의해서 문서간의 의존성(dependency) 분석을 지원하고 문장간 유사도 측정에 의해서 요구 사항간의 연계성(traceability), 중복성(redundancy), 불일치성(inconsistency), 그리고 불완전성(imcompleteness)을 발견하는 것을 지원한다. 또한 모호한 문장을 추출하여 요구사항의 불명확성 (ambiguity)을 발견하는 기능도 제공한다. 문서간 유사도 측정을 위해서 사용된 색인 방법은 슬라이딩 윈도우 모델과 의존 구조 모델을 결합한 것으로 각 모델이 가지는 단점을 효과적으로 보완할 수 있다. 본 논문에서는 문서간, 문장간 유사도 측정 기법의 효율성을 실험을 통해 검증하였으며 구현된 시스템을 통해 분석 처리되는 과정을 보여주고 있다.

Keywords

References

  1. Maarek Y., Berry D. and Kaiser G, An Information Retrieval Approach For Automatically Construction Software Libraries, IEEE Transaction On Software Engineering, Vol. 17, No, 8, pp.800-813, August 1991 https://doi.org/10.1109/32.83915
  2. Palmer J. and Liang Y., Indexing and clustering of software requirements specifications, Information and decision Technologies, Vol 18, pp.283-299, 1992
  3. Hearst M., 'Multi-Paragraph Segmentation of Expository Text,' Proceedings of the ACL'94, June 1994 https://doi.org/10.3115/981732.981734
  4. Litman D. and Passonneau R., 'Combining Multiple Knowledge Sources for Discourse Segmentation,' Proceedings of the 33rd ACL, May 1995 https://doi.org/10.3115/981658.981673
  5. Kozima H., 'Text Segmentation Based on Similarity between Words,' Proceedings of ACL'93, pp.286-288, January 1993 https://doi.org/10.3115/981574.981616
  6. Yaari Y., 'Segmentation of Expository Texts by Hierarchical Agglomerative Clustering,' Proceedings of RANLP'97, pp.135-142, September, 1997
  7. Jobbins A. and Evett L., 'Text Segmentation Using Reiteration and Collocation,' Proceedings of the COLING-ACL'98, pp.614-618, August 1998 https://doi.org/10.3115/980451.980947
  8. Hajime M., Takeo H. and Manabu O., 'Text Segmentation with Multiple Surface Linguistic Cues,' Proceedings of the COLING-ACL'98, pp.881-885, August 1998 https://doi.org/10.3115/980691.980714
  9. Kim M., Klavans J. and McKeown K., 'Linear Segmentation and Segment Significance,' Proceedings of the 6th International Workshop of Very Large Corpora(WVLC-6), pp.197-205, August, 1998
  10. Hellwig P., 'Dependency Unification Grammar,' Proceedings of COLLING86, pp.195-198, 1986 https://doi.org/10.3115/991365.991423
  11. Mel'cuk I. A., Dependency Syntax: Theory and Practice, State Univ. of New York Press, 1988
  12. Martin W.J.R, Al B. P. F., and van Sterkenburg P. J. G., 'On the processing of a text corpus: From textual data to lexicographic information,' In Lexicography: Principles and Practice (Applied Language Studies Series), Hartmann R. R. K., Ed. London: Academic, 1983
  13. Salton G. and McGill M.J., Introduction to Modern Information Retrieval (Computer Series), New York:McGraw-Hill, 1983
  14. Ash R.. Information Theory, New York:Wiley-Interscience, 1965