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On applying machine learning techniques for design pattern detection
Ist Teil von
The Journal of systems and software, 2015-05, Vol.103, p.102-117
Ort / Verlag
New York: Elsevier Inc
Erscheinungsjahr
2015
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
•We apply machine learning to detect design patterns in software systems.•We exploit a specific design pattern model to apply machine learning techniques.•We compare the performances of several machine learning algorithms.•We provide a large dataset containing manually checked design pattern instances.
The detection of design patterns is a useful activity giving support to the comprehension and maintenance of software systems. Many approaches and tools have been proposed in the literature providing different results. In this paper, we extend a previous work regarding the application of machine learning techniques for design pattern detection, by adding a more extensive experimentation and enhancements in the analysis method. Here we exploit a combination of graph matching and machine learning techniques, implemented in a tool we developed, called MARPLE-DPD. Our approach allows the application of machine learning techniques, leveraging a modeling of design patterns that is able to represent pattern instances composed of a variable number of classes. We describe the experimentations for the detection of five design patterns on 10 open source software systems, compare the performances obtained by different learning models with respect to a baseline, and discuss the encountered issues.