Detection of design pattern instances based on graph isomorphism

Yu, Daren; Ge, Jianlin; Wu, Wei

doi:10.1109/icsess.2013.6615444

Cited by 8 publications

(1 citation statement)

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“…Design pattern detection using structural analysis: This approach depends on transforming both the system under study and the design patterns into intermediate structures such as graphs and matrices. After that, the detection algorithm looks within the system structure for structures that represent specific design patterns [9,13,[21][22][23][24][25][26][27][28][29]. Methods that depend on structural analysis have high accuracy [10,11].…”

Section: Detection Approachesmentioning

confidence: 99%

A Feature-Based Method for Detecting Design Patterns in Source Code

Kouli

Rasoolzadegan

2022

Symmetry

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Design patterns are common solutions to existing issues in software engineering. In recent decades, design patterns have been researched intensively because they increase the quality factors of software systems such as flexibility, maintainability, and reusability. Design pattern detection refers to the determination of the symmetry between a code fragment and the definition of a design pattern. One of the major challenges in design pattern detection is how to obtain accurate information about the design patterns used in the software system due to the existence of different design pattern variants. Increasing the number of design pattern variants covered by a detection method is one of the main factors that increase its accuracy. In this paper, a step toward solving this challenge was taken by proposing a new feature-based method that builds on concrete definitions of existing design pattern variants and supports the definition and detection of new variants. In this proposed method, the needed features are extracted from the signatures of the design patterns. This method was applied to the 23 Gang of Four (GoF) design patterns and evaluated using four open-source Java projects. Afterward, it was compared with some previous methods using automatically generated testbeds. The experimental results demonstrated that the proposed method has better performance in terms of precision and recall compared to the other methods.

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