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
Kertas kerja ini membentangkan model seni bina Siam dengan dua Rangkaian Neural Convolutional (CNN) yang serupa untuk mengenal pasti klon kod; dua serpihan kod diwakili sebagai Abstract Syntax Trees (AST), subrangkaian berasaskan CNN mengekstrak vektor ciri daripada AST serpihan kod berpasangan, dan lapisan output menghasilkan betapa serupa atau tidak serupanya. Keputusan eksperimen menunjukkan bahawa pengekstrakan ciri berasaskan CNN berkesan dalam mengesan klon kod pada tahap kod sumber atau kod bait.
Dong Kwan KIM
Mokpo National Maritime University
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Salinan
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
Salinan
@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},}
Salinan
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 -