A Design Pattern Detection Method Based on Similarity Scoring

Wang Lei, Song Huina, Wang Wenfa

Abstract

 Detecting design pattern instances in UML models by using software tools can help software developers to understand,maintain and reconstruct the large-scale software projects. Most of the existing methods for automatic design pattern detection let the several considered features match separately,so that the accuracy rate and time performance are not high enough. Therefore,a design pattern detection method based on similarity scoring was proposed. First,the basic process of this method was given; then,a representation of system and patterns based on directed graph/matrix was proposed; the pattern instance search algorithm based on similarity scoring was discussed in detail; finally,a supporting tool for this method was implemented,and design patterns in an open source project were detected by using this tool. This method does not let the several considered features match separately,but uses the integral feature matrixes to match. The experimental results show that,compared with the design pattern detection methods which let the several considered features match separately,the detection accuracy rate and time performance of this method are higher.

 

 

Keywords:  design pattern detection,  accuracy,  time performance,  directed graph,  software re-engineering


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References


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