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a requirement traceability refinement method based on relevance feedback
Kong Lingjun; Li Juan; Li Yin; Yang Ye; Wang Qing
2009
Conference Name21st International Conference on Software Engineering and Knowledge Engineering, SEKE 2009
SourceProceedings of the 21st International Conference on Software Engineering and Knowledge Engineering, SEKE 2009
Conference Date44013
Conference PlaceBoston, MA, United states
Indexed TypeEI
Publish PlaceUnited Kingdom
ISBN1891706241
Department(1) Laboratory for Internet Software Technologies, Institute of Software, China; (2) Graduate University, Chinese Academy of Sciences, China
English AbstractIn this paper, we conduct a study of using relevance feedback-based Information Retrieval (IR) methods to refine Requirement Traceability (RT) from requirement to code. We compare two representative feedback methods: Mixture Model (MM) in language model and Standard Rochio method (SR) in vector-space model. In order to assure the fairness of comparison, we also make modification for both of the methods. Initial experiment results on a real project data set show that 1) few iterations of feedback result in significant increases both in precision and recall; 2) feedback methods in language model are generally more stable than methods in vector-space model in improving precision, but the latter is more effective and can get better precision; 3) negative feedback information plays an important role in refining requirement traceability.
KeywordComputational Linguistics Knowledge Engineering Refining Software Engineering Vector Spaces
SponsorshipKnowledge Systems Institute Graduate School
Content Type会议论文
URIhttp://ir.iscas.ac.cn/handle/311060/8422
Collection互联网软件技术实验室
Recommended Citation
GB/T 7714
Kong Lingjun,Li Juan,Li Yin,et al. a requirement traceability refinement method based on relevance feedback[C]. United Kingdom,2009.
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