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Software design patterns are standard solutions to common problems in software design and architecture. Knowing that a particular module implements a design pattern is a shortcut to design comprehension. Manually detecting design patterns is a time consuming and challenging task, therefore, researchers have proposed automatic design pattern detection techniques. However, these techniques show low performance for certain design patterns. In this work, we introduce a design pattern detection approach, DPD F that improves the performance over the state-of-the-art by using code features with machine learning classifiers to automatically train a design pattern detector. DPD F creates a semantic representation of Java source code using the code features and the call graph, and applies the Word2Vec algorithm on the semantic representation to construct the word-space geometric model of the Java source …