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Design Pattern Detection in Code : A Hybrid Approach Utilizing a Bayesian Network, Machine Learning with Graph Embeddings, and Micropattern Rules

  • Software design patterns and the abstractions they offer can support developers and maintainers with program code comprehension. Yet manually-created pattern documentation within code or code-related assets, such as documents or models, can be unreliable, incomplete, and labor-intensive. While various Design Pattern Detection (DPD) techniques have been proposed, industrial adoption of automated DPD remains limited. This paper contributes a hybrid DPD solution approach that leverages a Bayesian network integrating developer expertise via rule-based micropatterns with our machine learning subsystem that utilizes graph embeddings. The prototype shows its feasibility, and the evaluation using three design patterns shows its potential for detecting both design patterns and variations.

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Metadaten
Author:Roy OberhauserORCiD, Sandro Moser
URN:urn:nbn:de:bsz:944-opus4-32541
URL:https://www.thinkmind.org/index.php?view=article&articleid=icsea_2023_1_200_10112
Source Title (English):Proceedings of the Eighteenth International Conference on Software Engineering Advances
Conference Name:ICSEA
Conference Date:13-17 November
Conference Place:Valencia, Spain
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
Release Date:2024/01/16
Tag:artificial neural networks; graph embeddings; machine learning; rule-based expert system; software design pattern detection
Number of Pages:8
First Page:122
Last Page:129
Faculty:Elektronik und Informatik
Open Access:Open Access
Relevance:peer reviewed
Licence (German):License LogoUrheberrechtlich gesch├╝tzt