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A Deep Neural Network-Based Approach to Finding Similar Code Segments

Dong Kwan KIM

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Summary :

This paper presents a Siamese architecture model with two identical Convolutional Neural Networks (CNNs) to identify code clones; two code fragments are represented as Abstract Syntax Trees (ASTs), CNN-based subnetworks extract feature vectors from the ASTs of pairwise code fragments, and the output layer produces how similar or dissimilar they are. Experimental results demonstrate that CNN-based feature extraction is effective in detecting code clones at source code or bytecode levels.

Publication
IEICE TRANSACTIONS on Information Vol.E103-D No.4 pp.874-878
Publication Date
2020/04/01
Publicized
2020/01/17
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDL8195
Type of Manuscript
LETTER
Category
Software Engineering

Authors

Dong Kwan KIM
  Mokpo National Maritime University

Keyword