A Component Retrieval Method Based on Facet-Weight Self-learning

  • Xiaoqin Xie
  • Jie Tang
  • Juanzi Li
  • Kehong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3309)

Abstract

Component-based development method has been a new software development paradigm. How to get the needed components quickly and accurately is one of the basic problems about reusing software component automatically. In this paper, an intelligent component retrieval model – FWRM. is proposed. Facet presentation is used to model query and component. Multiple types of facets are defined which extends traditional keyword-based facet presentation. Genetic algorithm based facet weight self-learning algorithm can change the facet weight dynamically in order to improve retrieval accuracy. Corresponding similarity functions are defined also. In addition, risk minimization-based component sampling method is used to solve the insufficiency of training data. All these algorithms and methods are integrated into FWRM’s three main implementation parts: Facet-Weight Optimize System, Component Retrieve System and Resource. The experimental results prove that this method is feasible and can improve component retrieval effectively.

Keywords

Software Component Retrieval Model Plain Text Component Library Facet Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Xiaoqin Xie
    • 1
  • Jie Tang
    • 1
  • Juanzi Li
    • 1
  • Kehong Wang
    • 1
  1. 1.Knowledge Engineering Group, Computer Science DepartmentTsinghua UniversityBeijingP.R.China

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