Feature-based software design pattern detection

Abstract

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, DPDF 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. DPDF 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 code. DPDF then builds a machine learning classifier trained on a labelled dataset and identifies software design patterns with over 80% Precision and over 79% Recall. Additionally, we have compared DPDF with two existing design pattern detection techniques namely FeatureMaps &MARPLE-DPD. Empirical results demonstrate that our approach outperforms the existing approaches by approximately 35% and 15% respectively in terms of Precision. The run-time performance also supports the practical applicability of our classifier.