Deep Learning Anti-patterns from Code Metrics History

TL;DR

This paper introduces CAME, a deep learning approach that combines structural and historical code metrics to improve the detection of anti-patterns, specifically the God Class anti-pattern, outperforming existing methods.

Contribution

CAME is the first deep learning method to integrate both structural and historical code metrics for anti-pattern detection, enhancing accuracy over prior approaches.

Findings

Using historical metrics improves detection precision.

CAME outperforms existing static classifiers.

CAME surpasses current anti-pattern detection tools.

Abstract

Anti-patterns are poor solutions to recurring design problems. Number of empirical studies have highlighted the negative impact of anti-patterns on software maintenance which motivated the development of various detection techniques. Most of these approaches rely on structural metrics of software systems to identify affected components while others exploit historical information by analyzing co-changes occurring between code components. By relying solely on one aspect of software systems (i.e., structural or historical), existing approaches miss some precious information which limits their performances. In this paper, we propose CAME (Convolutional Analysis of code Metrics Evolution), a deep-learning based approach that relies on both structural and historical information to detect anti-patterns. Our approach exploits historical values of structural code metrics mined from version control systems and uses a Convolutional Neural Network classifier to infer the presence of anti-patterns from this information. We experiment our approach for the widely known God Class anti-pattern and evaluate its performances on three software systems. With the results of our study, we show that: (1) using historical values of source code metrics allows to increase the precision; (2) CAME outperforms existing static machine-learning classifiers; and (3) CAME outperforms existing detection tools.