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Title: TECCD: A Tree Embedding Approach for Code Clone Detection
Clone detection techniques have been explored for decades. Recently, deep learning techniques has been adopted to improve the code representation capability, and improve the state-of-the-art in code clone detection. These approaches usually require a transformation from AST to binary tree to incorporate syntactical information, which introduces overheads. Moreover, these approaches conduct term-embedding, which requires large training datasets. In this paper, we introduce a tree embedding technique to conduct clone detection. Our approach first conducts tree embedding to obtain a node vector for each intermediate node in the AST, which captures the structure information of ASTs. Then we compose a tree vector from its involving node vectors using a lightweight method. Lastly Euclidean distances between tree vectors are measured to determine code clones. We implement our approach in a tool called TECCD and conduct an evaluation using the BigCloneBench (BCB) and 7 other large scale Java projects. The results show that our approach achieves good accuracy and recall and outperforms existing approaches.  more » « less
Award ID(s):
1816594
PAR ID:
10194570
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
IEEE International Conference on Software Maintenance and Evolution (ICSME)
Page Range / eLocation ID:
145 to 156
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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