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Feature-based software design pattern detection
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DOI：10.1016/j.jss.2021.111179.png)
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摘要
En 中文
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. (c) 2021 Elsevier Inc. All rights reserved.
Keyword:
Software design patterns
Code features
Word-space-model
Machine learning
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