A comprehensive approach to the recovery of design pattern instances based on sub-patterns and method signatures

Yu, Daren; Zhang, Yanyan; Chen, Zhenli

doi:10.1016/j.jss.2015.01.019

Cited by 42 publications

(25 citation statements)

References 32 publications

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“…Since a PLV aims to verify the application of the patterns of a given PL, ie, the correct application of single patterns as well as the pattern interrelationships, we review the related works only from perspective of the application of patterns. We do not consider the researches that are related to pattern identification and detection, which is a more reverse engineering task (see Rouhi and Zamani for more details on existing pattern identification and detection tools). According to the above mentioned filters, we have found several related works, which are addressed in the following.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

A model‐based framework for automatic generation of a pattern language verifier

Rouhi

Zamani

2017

Softw Pract Exp

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Summary Design patterns solve recurring design problems. One of the best practices, to solve a whole problem in a specific context, is to apply patterns in the form of a collection of related patterns, ie, a pattern language (PL). However, with the rapid growth of the number of patterns and their interrelationships in PLs and because manual verification of the applied PLs is a tedious and error‐prone task, when designing in PL‐based fashion, there is a need for tool support to verify the applied PLs. In our previous work, aimed at automating the development process of PL verification tools, a PL formalism was presented. This paper, based on the presented formalism, describes a framework called Pattern Language Verifier Generator (PLVGen) that automatically generates a pattern language verifier (PLV) for any given PL assuming its constituent patterns are represented in the Unified Modeling Language (UML). Using a modelware approach, two metamodels named pattern language specification language (PLSL) and pattern specification language (PSL) are developed to model the given PL and its patterns, respectively. By getting the models of a PL and its patterns, PLVGen automatically generates an epsilon validation language (EVL) module representing the expected PLV. By launching PLV, the applied PL on a UML model will be verified. To evaluate the applicability of PLVGen in practice, we automatically generated three PLVs. The statistics regarding the generated PLVs, as case studies, illustrate the scalability of PLVGen. Compared with similar tools, the generated PLVs are more portable, transparent, and free of ambiguities.

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Section: Related Work and Backgroundmentioning

confidence: 99%

A model‐based framework for automatic generation of a pattern language verifier

Rouhi

Zamani

2017

Softw Pract Exp

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“…One of the common approaches to pattern detection in code is detecting patterns based on their signatures. So that, detecting each pattern in code relies on a number of structural and behavioral criteria (Chihada et al, 2014;Dongjin et al, 2015;Fontana et al, 2011a, b). The set of criteria for each pattern forms its signature.…”

Section: Introductionmentioning

confidence: 99%

“…[Method] = ConcreteCreator. [Method]⇒ Product" is a sample method signature for the Factory Method pattern (Gamma et al, 1995;Dongjin et al, 2015;Lucia et al, 2010a, b;Walter, 1995;Binun, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…A major factor contributing to the accuracy of any detection method is the used signatures. Many signatures are merely based on the standard structure of patterns, without considering the variants of each pattern Yu et al, 2013;Dongjin et al, 2015;Fontana et al, 2011a, b). This causes only the instances of each pattern to be detected that conform to the standard structure of the corresponding pattern, and if there is a variant of a pattern in the code that satisfies the purpose of the pattern but is different from the standard structure of the pattern, do not be detected.…”

Section: Introductionmentioning

confidence: 99%

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A new method for detecting various variants of GoF design patterns using conceptual signatures

et al. 2021

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Software design patterns are well-known solutions for solving commonly occurring problems in software design. Detecting design patterns used in the code can help to understand the structure and behavior of the software, evaluate the quality of the software, and trace important design decisions. To develop and maintain a software system, we need sufficient knowledge of design decisions and software implementation processes. However, the acquisition of knowledge related to design patterns used in complex software systems is a challenging, time-consuming, and costly task. Therefore, using a suitable method to detect the design patterns used in the code reduces software development and maintenance costs. In this paper, we proposed a new method based on conceptual signatures to improve the accuracy of design pattern detection. So we used the conceptual signatures based on the purpose of patterns to detect the patterns' instances that conform to the standard structure of patterns, and cover more instances of patterns' variants and implementation versions of the patterns and improve the accuracy of pattern detection. The proposed method is a specific process in two main phases. In the first phase, the conceptual signature and detection formula for each pattern is determined manually. Then in the second phase, each pattern in the code is detected in a semi-automatic process using the conceptual signature and pattern detection formula. To implement the proposed method, we focused on GoF design patterns and their variants. We evaluated the accuracy of our proposed method on five open-source projects, namely, Junit v3.7, JHotDraw v5.1, QuickUML 2001, JRefactory v2.6.24, and MapperXML v1.9.7. Also, we performed our experiments on a set of source codes containing the instances of GoF design patterns' variants for a comprehensive and fair evaluation. The evaluation results indicate that the proposed method has improved the accuracy of design pattern detection in the code.

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“…Specifically, we used the most recent version of the tool, v4.12. It was verified against several Java systems with a reported precision of 100% and a recall of 66.7-100 % [3], which makes its performance comparable to other approaches that use exact/inexact graph match-350 ing like Discovery Matrix (DP-Miner) [12], the sub-patterns approach [56], or metrics-based approaches, e.g., MAISA [40] and FUJABA [36]. Although the tool detects only selected GoF patterns, they cover all three categories in the 355 GoF taxonomy: creational, structural and behavioral [18].…”

mentioning

confidence: 99%