Design pattern recovery through visual language parsing and source code analysis

Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques; however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.

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“…Some detection techniques use a multilayered (multiphase) approach [13] [14]. Lucia et al use a two-phase, static analysis approach [13].…”

Section: Conventional Techniques and Their Problemsmentioning

confidence: 99%

“…Lucia et al use a two-phase, static analysis approach [13]. This method has difficulty, however, in detecting creational and behavioral patterns because it analyzes pattern structures and source code level conditions.…”

Section: Conventional Techniques and Their Problemsmentioning

confidence: 99%

Detecting Design Patterns in Object-Oriented Program Source Code by Using Metrics and Machine Learning

Uchiyama

Kubo

Washizaki

et al. 2014

JSEA

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“…As mentioned in (Rasool and Streitferdt 2011) the flexibility in software maintenance and reusability motivated several researchers to develop Design Patterns recovery techniques. Examples of such techniques can be found in (Dong et al 2009;Gueheneuc and Antoniol 2008;Lucia et al 2009;Tsantalis et al 2006) and in more recent work, such as Elaasar et al (2015). The recovery of Design Patterns mainly aims to identify with high accuracy the Design Patterns that were used in existing software tools.…”

Section: Design Patterns Recoverymentioning

confidence: 99%

ArchReco: a software tool to assist software design based on context aware recommendations of design patterns

Sielis

Tzanavari

Papadopoulos

2017

J Softw Eng Res Dev

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This work describes the design, development and evaluation of a software Prototype, named ArchReco, an educational tool that employs two types of Context-aware Recommendations of Design Patterns, to support users (CS students or professionals) who want to improve their design skills when it comes to training for High Level Software models. The tool's underlying algorithms take advantage of Semantic Web technologies, and the usage of Content based analysis for the computation of non-personalized recommendations for Design Patterns. The recommendations' objective is to support users in functions such as finding the most suitable Design Pattern to use according to the working context, learn the meaning, objectives and usages of each Design Pattern. The current work presents the Semantic Modeling of the Software Design process through the definition of the context that defines the Software Design process and in particular the representation of the Design Patterns as Ontology model, the implemented Context Aware Recommendation Algorithms and the evaluation results extracted from a user based testing for the ArchReco prototype.

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“…On the one hand, in general the use of metamodels has shown to improve rigor and communication between developers, and facilitates system integration and interoperability [30,31]. On the other hand, the fact that the OcQuery Language is defined strictly according to the concepts established in our metamodel promotes its correct use in systems defined under such a metamodel.…”

Section: Analysis and Evaluation Of The Proposalmentioning

confidence: 99%

Developing provenance-aware query systems: an occurrence-centric approach

et al. 2016

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In recent years, research on provenance has increased exponentially, and such studies in the field of business process monitoring have been especially remarkable. Business process monitoring deals with recording information about the actual execution of processes to then extract valuable knowledge that can be utilized for business process quality improvement. In prior research, we developed an occurrence-centric approach built on our notion of occurrence that provides a holistic perspective of system dynamics. Based on this concept, more complex structures are defined herein, namely Occurrence Base (OcBase) and Occurrence Management System (OcSystem), which serve as scaffolding to develop business process monitoring systems. This paper focuses primarily on the critical provenance task of extracting valuable knowledge from such systems by proposing an Occurrence Query Framework that includes the definition of an Occurrence Base Metamodel and an Occurrence Query Language based on this metamodel. Our framework provides a way of working for the construction of business process monitoring systems that are provenance aware. As a proof of concept, a tool implementing the various components of the framework is presented. This tool has been tested against a real system in the context of biobanks.

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Design pattern recovery through visual language parsing and source code analysis

Cited by 75 publications

References 34 publications

Detecting Design Patterns in Object-Oriented Program Source Code by Using Metrics and Machine Learning

Detecting Design Patterns in Object-Oriented Program Source Code by Using Metrics and Machine Learning

ArchReco: a software tool to assist software design based on context aware recommendations of design patterns

Developing provenance-aware query systems: an occurrence-centric approach

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