An ensemble learning approach for software semantic clone detection

TL;DR

This paper presents an ensemble learning method utilizing word embeddings to improve the detection of semantic code clones, addressing limitations of previous syntactic and deep learning approaches.

Contribution

It introduces a novel ensemble approach combining word embeddings and machine learning for semantic clone detection, enhancing accuracy over existing methods.

Findings

Significantly improved precision and recall on BigCloneBench

Outperforms SourcererCC and CDLH in semantic clone detection

Effective in identifying functionally similar code without syntactic similarity

Abstract

Code clone is a serious problem in software and has the potential to software defects, maintenance overhead, and licensing violations. Therefore, clone detection is important for reducing maintenance effort and improving code quality during software evolution. A variety of clone detection techniques have been proposed to identify similar code in software. However, few of them can efficiently detect semantic clones (functionally similar code without any syntactic resemblance). Recently, several deep learning based clone detectors are proposed to detect semantic clones. However, these approaches have high cost in data labelling and model training. In this paper, we propose a novel approach that leverages word embedding and ensemble learning techniques to detect semantic clones. Our evaluation on a commonly used clone benchmark, BigCloneBench, shows that our approach significantly improves the precision and recall of semantic clone detection, in comparison to a token-based clone detector, SourcererCC, and another deep learning based clone detector, CDLH.