IEICE TRANSACTIONS on Information
A Deep Neural Network-Based Approach to Finding Similar Code Segments
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
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Dong Kwan KIM, "A Deep Neural Network-Based Approach to Finding Similar Code Segments" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 4, pp. 874-878, April 2020, doi: 10.1587/transinf.2019EDL8195.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8195/_p
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@ARTICLE{e103-d_4_874,
author={Dong Kwan KIM, },
journal={IEICE TRANSACTIONS on Information},
title={A Deep Neural Network-Based Approach to Finding Similar Code Segments},
year={2020},
volume={E103-D},
number={4},
pages={874-878},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019EDL8195},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - A Deep Neural Network-Based Approach to Finding Similar Code Segments
T2 - IEICE TRANSACTIONS on Information
SP - 874
EP - 878
AU - Dong Kwan KIM
PY - 2020
DO - 10.1587/transinf.2019EDL8195
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2020
AB - 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.
ER -
English
