A Multiple Phases Approach for Design Patterns Recovery Based on Structural and Method Signature Features

Author:

Al-Obeidallah Mohammed Ghazi1,Petridis Miltos2,Kapetanakis Stelios1

Affiliation:

1. Department of Computing, University of Brighton, Brighton, UK

2. Department of Computing, Middlesex University, London, UK

Abstract

Design patterns describe both structure, behavior of classes and their relationships. They can improve software documentation, speed up the development process and enable large-scale reuse of software architectures. This article presents a multiple levels detection approach (MLDA) to recover design pattern instances from Java source code. MLDA is able to recover design pattern instances based on a generated class level representation of a subject system. Specifically, MLDA presents what is the so-called Structural Search Model (SSM) which incrementally builds the structure of each design pattern based on the generated source code model. Moreover, MLDA uses a rule-based approach to match the method signatures of the candidate design instances to that of the subject system. As the experiment results illustrate, MLDA is able to recover 23 design patterns with reasonable detection accuracy.

Publisher

IGI Global

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Computer Science Applications,Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards a Framework to Assess the Impact of Design Patterns on Software Metrics;2023 International Conference on Multimedia Computing, Networking and Applications (MCNA);2023-06-19

2. A Benchmark for Design Pattern Recovery Tools;2023 International Conference on Software and System Engineering (ICoSSE);2023-04

3. Features and Supervised Machine Learning Based Method for Singleton Design Pattern Variants Detection;Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering;2023

4. A new method for detecting various variants of GoF design patterns using conceptual signatures;Software Quality Journal;2021-11-29

5. GEML: A grammar-based evolutionary machine learning approach for design-pattern detection;Journal of Systems and Software;2021-05

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