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

被引：30

作者：

Lei, Maggie ^{[1
]}

Li, Hao ^{[1
]}

Li, Ji ^{[1
]}

Aundhkar, Namrata ^{[1
]}

Kim, Dae-Kyoo ^{[1
]}

机构：

[1] Oakland Univ, Comp Sci & Engn, Rochester, MI 48309 USA

来源：

JOURNAL OF SYSTEMS AND SOFTWARE | 2022年 / 184卷

关键词：

Code clone; Code smell; Deep learning; Duplicate code; Machine learning; Literature review;

D O I：

10.1016/j.jss.2021.111141

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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. (C) 2021 Elsevier Inc. All rights reserved.

引用

页数：15

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