The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article présente un modèle d'architecture siamoise avec deux réseaux de neurones convolutifs (CNN) identiques pour identifier les clones de code ; deux fragments de code sont représentés sous forme d'arbres de syntaxe abstraite (AST), les sous-réseaux basés sur CNN extraient les vecteurs de caractéristiques des AST de fragments de code par paires, et la couche de sortie détermine leur similarité ou leur différence. Les résultats expérimentaux démontrent que l'extraction de fonctionnalités basée sur CNN est efficace pour détecter les clones de code au niveau du code source ou du bytecode.
Dong Kwan KIM
Mokpo National Maritime University
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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},}
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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 -