Feature-Based Software Design Pattern Detection

TL;DR

This paper presents DPD_F, a machine learning-based approach that improves automatic detection of software design patterns in Java code by leveraging code features, semantic representations, and the Word2Vec algorithm, achieving high precision and recall.

Contribution

The paper introduces DPD_F, a novel design pattern detection method that outperforms existing techniques using semantic code features and machine learning classifiers.

Findings

DPD_F achieves over 80% precision and 79% recall.

Outperforms FeatureMaps and MARPLE-DPD by 35% and 15% in precision.

Demonstrates practical runtime performance for real-world use.

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, 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 \textit{Word2Vec} algorithm on the semantic representation to construct the word-space geometric model of the Java source code. DPD$_F$ 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 DPD_F with two existing design pattern detection techniques namely FeatureMaps & MARPLE-DPD. Empirical results demonstrate that our approach outperforms the state-of-the-art approaches by approximately 35% and 15% respectively in terms of Precision. The run-time performance also supports the practical applicability of our classifier.