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 patterns detection techniques to facilitate software developers. However, these techniques show low performance for certain design patterns. In this work, we introduce an approach that improves the performance over the state-of-the-art by using code features with machine learning classifiers to automatically train a design pattern detection. We create a semantic representation of source code from the code features and the call graph, and apply the Word2Vec algorithm on the semantic representation to construct the word-space geometric model of the Java source code. DPD_F then uses a Machine Learning approach trained using the word-space model and identifies software design patterns with 74% Precision and 71% Recall. Additionally, we have compared our results with two existing design pattern detection approaches namely FeatureMaps & MARPLE-DPD. Empirical results demonstrate that our approach outperforms the benchmark approaches by 30% and 10% respectively in terms of Precision. The runtime performance also supports its practical applicability.
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