Home // ICSEA 2020, The Fifteenth International Conference on Software Engineering Advances // View article
Authors:
Roy Oberhauser
Keywords: software design pattern detection; machine learning; artificial neural networks; software engineering.
Abstract:
As the amount of software source code increases, manual approaches for documentation or detection of software design patterns in source code become inefficient relative to the value. Furthermore, typical automatic pattern detection tools are limited to a single programming language. To address this, our Design Pattern Detection using Machine Learning (DPDML) offers a generalized and programming language agnostic approach for automated design pattern detection based on machine learning (ML). The focus of our evaluation was on ensuring DPDML can reasonably detect one design pattern in the structural, creational, and behavioral category for two popular programming languages (Java and C#). 60 unique Java and C# code projects were used to train the artificial neural network (ANN) and 15 projects were then used to test pattern detection. The results show the feasibility and potential for pursuing an ANN approach for automated design pattern detection.
Pages: 27 to 32
Copyright: Copyright (c) IARIA, 2020
Publication date: October 18, 2020
Published in: conference
ISSN: 2308-4235
ISBN: 978-1-61208-827-3
Location: Porto, Portugal
Dates: from October 18, 2020 to October 22, 2020