Deep learning application on code clone detection: A review of current knowledge

Abstract

Bad smells in code are indications of low code quality representing potential threats to the maintainability and reusability of software. Code clone is a type of bad smells caused by code fragments that have the same functional semantics with syntactic variations. In the recent years, the research on duplicate code has been dramatically geared up by deep learning techniques powered by advances in computing power. However, there exists little work studying the current state-of-art and future prospects in the area of applying deep learning to code clone detection. In this paper, we present a systematic review of the literature on the application of deep learning on code clone detection. We aim to find and study the most recent work on the subject, discuss their limitations and challenges, and provide insights on the future work.