Welcome to the InfoSci Platform
Reference Hub5
A Multiple Phases Approach for Design Patterns Recovery Based on Structural and Method Signature Features

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

Mohammed Ghazi Al-Obeidallah, Miltos Petridis, Stelios Kapetanakis
Copyright: © 2018 |Volume: 6 |Issue: 3 |Pages: 17
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781522546856|DOI: 10.4018/IJSI.2018070103
Cite Article Cite Article

MLA

Al-Obeidallah, Mohammed Ghazi, et al. "A Multiple Phases Approach for Design Patterns Recovery Based on Structural and Method Signature Features." IJSI vol.6, no.3 2018: pp.36-52. http://doi.org/10.4018/IJSI.2018070103

APA

Al-Obeidallah, M. G., Petridis, M., & Kapetanakis, S. (2018). A Multiple Phases Approach for Design Patterns Recovery Based on Structural and Method Signature Features. International Journal of Software Innovation (IJSI), 6(3), 36-52. http://doi.org/10.4018/IJSI.2018070103

Chicago

Al-Obeidallah, Mohammed Ghazi, Miltos Petridis, and Stelios Kapetanakis. "A Multiple Phases Approach for Design Patterns Recovery Based on Structural and Method Signature Features," International Journal of Software Innovation (IJSI) 6, no.3: 36-52. http://doi.org/10.4018/IJSI.2018070103

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.