A Semantic Web based approach for design pattern detection from source code

Paydar, Samad; Kahani, Mohsen

doi:10.1109/iccke.2012.6395394

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…1: UML Diagram and corresponding SPARQL query for a simple inheritance relationship accuracy of mined queries. Our results thus far indicate that they are also comparable, or perform better, than existing techniques [5], [6], [9], [12], [15], [16], [17], [18], [30].…”

Section: Introductionsupporting

confidence: 57%

“…Earlier approaches for detecting design patterns in source code ranged from sub-graph matching [15], [16], [18] and ontology based techniques [6], [9] to using machine learning techniques [5], [12], [17], [30] and sequence diagrams [9]. A detailed meta-analysis of various design pattern mining approaches is discussed in [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are ontology-based approaches to mining design patterns (e.g., [6], [9]). One approach uses Semantic Web technologies for automatically detecting design patterns [6]. However, this requires manual specification of queries and rules.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meanwhile, other techniques also require manual work before design patterns could be mined. This may involve the time-consuming task of manual labeling training data [12] or manual specification of patterns for mining (e.g., rules [44], queries [6]). Another key challenge in automation is the variations in implementations of design patterns making direct pattern matching infeasible.…”

Section: Introductionmentioning

confidence: 99%

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Automated Query Generation for Design Pattern Mining in Source Code

Poozhithara¹,

Asuncion²,

Lagesse³

2020

Preprint

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Identifying which design patterns already exist in source code can help maintenance engineers gain a better understanding of the source code and determine if new requirements can be satisfied. There are current techniques for mining design patterns, but some of these techniques require tedious work of manually labeling training datasets, or manually specifying rules or queries for each pattern. To address this challenge, we introduce Model2Mine, a technique for automatically generating SPARQL queries by parsing UML diagrams, ensuring that all constraints are appropriately addressed. We discuss the underlying architecture of Model2Mine and its functionalities. Our initial results indicate that Model2Mine can automatically generate queries for the three types of design patterns (i.e., creational, behavioral, structural), with a slight performance overhead compared to manually generated queries, and accuracy that is comparable, or perform better than, existing techniques.

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Section: Introductionsupporting

confidence: 57%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Query Generation for Design Pattern Mining in Source Code

Poozhithara¹,

Asuncion²,

Lagesse³

2020

Preprint

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“…However, no considerations are made on patterns' behavioural characteristics, neither on the possibility to analyse a graphical representation of them. Semantic web technologies are also exploited in , where a methodology is presented for pattern recognition, which is based on an internal semantic data model representation of the source code and on SPARQL queries execution as the analysis mechanism. The work described in proposes a tool, in the form of an Eclipse plug‐in called Metrics and Architecture Reconstruction Plug‐in for Eclipse (MARPLE), which perfectly falls in the scenario of pattern recognition from source code.…”

Section: State Of Artmentioning

confidence: 99%

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

Martino

Esposito

2015

Softw. Pract. Exper.

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Summary In the present work, we describe a procedure and a prototype implementation for the automatic recognition of design patterns from documentation of software artefacts design and implementation, provided in a machine readable form, namely, the XML Metadata Interchange (XMI) coded representation of UML class diagrams. The procedure exploits a semantic representation of the patterns to be recognized, based on an existing Web Ontology Language (OWL), known as object design ontology layer (ODOL), defined by the University of Massey (New Zealand), which has been augmented with an OWL‐S based representation of the patterns' dynamic behaviour. Both the UML set of diagrams related to the analysed software artefacts and the ODOL+OWL‐S patterns representation are automatically scanned and translated into a first‐order logic representation (namely Prolog). A set of first‐order logic rules, independent from the specific pattern to be recognized, has been defined to describe the heuristics and features which trigger the recognition, exploiting the Prolog description of the patterns to be recognized and the base of Prolog facts, which represents the UML documentation. Copyright © 2015 John Wiley & Sons, Ltd.

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An SOA Design Patterns Recommendation System Based on Ontology

Abdelhedi

Bouassidar

2019

Advances in Intelligent Systems and Computing

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A Semantic Web based approach for design pattern detection from source code

Cited by 10 publications

References 15 publications

Automated Query Generation for Design Pattern Mining in Source Code

Automated Query Generation for Design Pattern Mining in Source Code

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

An SOA Design Patterns Recommendation System Based on Ontology

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