A rule-based procedure for automatic recognition of design patterns in UML diagrams

Martino, Beniamino Di; Esposito, Antonio

doi:10.1002/spe.2336

Cited by 17 publications

(5 citation statements)

References 35 publications

(40 reference statements)

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“…Lucia et al, develop a DPRE tool [29] based also on reverse engineering through visual parsing of diagrams. Another work proposed by [30] explore the detection of design pattern on UML class diagrams by the use of first-order logic representation.…”

Section: Related Workmentioning

confidence: 99%

Features and Supervised Machine Learning-Based Method for Singleton Design Pattern Variants Detection

Nacef

Bahroun

Khalfallah

et al. 2022

Preprint

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Design patterns codify standard solutions to common problems in software design and architecture. Given their importance in improving software quality and facilitating code reuse, many types of research are proposed on their automatic detection. In this paper, we focus on singleton pattern recovery by proposing a method that can identify orthodox implementations and non-standard variants. The recovery process is based on specific data created using a set of relevant features. These features are specific information defining each variant which is extracted from the java program by structural and semantic analysis. We are based on the singleton analysis and different proposed features presented by Nacef et al. in [1] to create structured data. This data contains a combination of feature values defining each singleton variant to train a supervised Machine Learning (ML) algorithm. The goal is not limited to detecting the singleton pattern but also the specification of the implemented variant as so as the incorrect structure that is incoherent with the pattern intent. We use different ML algorithms to create the Singleton Detector (SD) to compare their performance. The empirical results demonstrate that our method for automatically training the (SD) based on features and supervised ML, can identify any singleton implementation with the specific variant’s name achieving 99% of precision, and recall. We have compared the proposed approach to similar studies namely DPDf and GEML. The results show that the SD outperforms the state-of-the-art approaches by more than 20% on evaluated data constructed from different repositories; PMART, DPB and DPDf corpus in terms of precision.

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Section: Related Workmentioning

confidence: 99%

Features and Supervised Machine Learning-Based Method for Singleton Design Pattern Variants Detection

Nacef

Bahroun

Khalfallah

et al. 2022

Preprint

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“…Design pattern detection using database queries: In the query-based approach, the source code of the input system is transformed into an intermediate structure such as AST, ASG, UML, and XMI. After that, database queries are applied to the intermediate structure to extract the information needed to detect the design patterns [35][36][37][38][39][40][41]. The accuracy of these methods depends on the used database and the information represented by the intermediate structure [9][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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“…Given that GEML performs static analysis, we mostly focused on these approaches for DP detection, with special attention to those proposals based on machine learning. In addition, even though the majority of studies take source code as input, other authors have explored detection at design stages too, e.g., using UML class diagrams (Di Martino and Esposito, 2016).…”

Section: Related Workmentioning

confidence: 99%

GEML: A grammar-based evolutionary machine learning approach for design-pattern detection

Barbudo

Ramírez

Servant

et al. 2021

Journal of Systems and Software

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A rule-based procedure for automatic recognition of design patterns in UML diagrams

Cited by 17 publications

References 35 publications

Features and Supervised Machine Learning-Based Method for Singleton Design Pattern Variants Detection

Features and Supervised Machine Learning-Based Method for Singleton Design Pattern Variants Detection

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

GEML: A grammar-based evolutionary machine learning approach for design-pattern detection

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