TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Oberhauser, Roy
A1  - Moser, Sandro
T1  - Design Pattern Detection in Code
BT  - A Hybrid Approach Utilizing a Bayesian Network, Machine Learning with Graph Embeddings, and Micropattern Rules
T2  - Proceedings of the Eighteenth International Conference on Software Engineering Advances
N2  - 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.
KW  - software design pattern detection
KW  - machine learning
KW  - artificial neural networks
KW  - graph embeddings
KW  - rule-based expert system
Y1  - 2023
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:944-opus4-32541
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:944-opus4-32541
UR  - https://www.thinkmind.org/index.php?view=article&articleid=icsea_2023_1_200_10112
SP  - 122
EP  - 129
S1  - 8
ER  -