Code Clone Detection Based on Transformer and Convolutional Neural Network

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Code Clone Detection Based on Transformer and Convolutional Neural Network

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[1]BEN Kerong,YANG Jiahui,ZHANG Xian,et al.Code Clone Detection Based on Transformer and Convolutional Neural Network[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):12-18.[doi:10.13705/j.issn.1671-6833.2023.03.012]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 06 Page number: 12-18 Column: Public date: 2023-12-25

Title:: Code Clone Detection Based on Transformer and Convolutional Neural Network

Author(s):: BEN Kerong; YANG Jiahui; ZHANG Xian; ZHAO Chong; College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

Keywords:: code clone detection; abstract syntax tree ( AST) ; Transformer; convolutional neural network; code feature extraction

CLC:: TP393

DOI:: 10.13705/j.issn.1671-6833.2023.03.012

Abstract:: Code clone detection, based on deep learning, is often applied to use the model to extract features in the sequence of tokens or the entire AST. That may lead to the missing of important semantic information and induce gradient disappearance. Aiming at these problems, a method of code clone detection based on Transformer and CNN was proposed. First of all, source code was parsed into AST. Then the AST was cut into statement subtrees, which were input into the neural network. Statement subtrees were composed of a sequence of statement nodes obtained by pre-traversal, which contained the structure and hierarchical information. In terms of neural network design, Encoder of Transformer was used to extract global information of the code. CNN was used to capture the local information. Fusion of features were extracted from two different networks. Finally, a vector containing lexical, syntax, and structural information could be learned. The Euclidean distance was used to represent the degree of semantic association. A classifier is trained to detect code clone. Experimental results showed that on OJClone dataset, the Precision, Recall, and F1 values could reach 98. 9%, 98. 1%, and 98. 5%, respectively. On BigCloneBench dataset, the Precision, Recall, and F1 values could reach 99. 1%, 91. 5%, and 94. 2%, respectively. Compared with the relevant methods, the Precision, Recall, and F1 values were all improved. This method could effectively detect code clone.

References:: [1] 陈秋远, 李善平, 鄢萌, 等. 代码克隆检测研究进展 [ J] . 软件学报, 2019, 30(4) : 962-980.
CHEN Q Y, LI S P, YAN M, et al. Code clone detection: a literature review[ J] . Journal of Software, 2019, 30(4) : 962-980.
[2] BELLON S, KOSCHKE R, ANTONIOL G, et al. Comparison and evaluation of clone detection tools[ J] . IEEE Transactions on Software Engineering, 2007, 33(9) : 577 -591.
[3] HINDLE A, BARR E T, SU Z D, et al. On the naturalness of software[ C]∥2012 34th International Conference on Software Engineering ( ICSE ) . Piscataway: IEEE, 2012: 837-847.
[4] CORDY J R, ROY C K. The NiCad clone detector [C]∥ 2011 IEEE 19th International Conference on Program Comprehension. Piscataway: IEEE, 2011: 219-220.
[5] LI L Q, FENG H, ZHUANG W J, et al. CCLearner: a deep learning-based clone detection approach [ C] ∥2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). Piscataway: IEEE, 2017: 249-260.
[6] WEI H H, LI M. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code[ C]∥Proceedings of the 26th International Joint Conference on Artificial Intelligence. New York: ACM, 2017: 3034-3040.
[7] ALON U, LEVY O, YAHAV E. CODE2SEQ: generatingsequences from structured representations of code [ EB / OL] . (2018- 08 - 04) [ 2022 - 09 - 11] . https:∥arxiv. org / abs/ 1808. 01400.
[8] ALON U, ZILBERSTEIN M, LEVY O, et al. CODE2VEC: learning distributed representations of code[ J] . Proceedings of the ACM on Programming Languages, 2019, 3: 40.
[9] ZENG J, BEN K R, LI X W, et al. Fast code clone detection based on weighted recursive autoencoders [ J ] . IEEE Access, 2019, 7: 125062-125078.
[10] ZHANG J, WANG X, ZHANG H Y, et al. A novel neural source code representation based on abstract syntax tree[C]∥2019 IEEE / ACM 41st International Conference on Software Engineering ( ICSE ) . Piscataway: IEEE, 2019: 783-794.
[11] MENG Y, LIU L. A deep learning approach for a source code detection model using self-attention [ J] . Complexity, 2020, 2020: 1-15.
[12] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[ C]∥Advances in Neural Information Processing Systems 30. Long Beach: NIPS, 2017:5998- 6008.
[13] CORDONNIER J B, LOUKAS A, JAGGI M. On the relationship between self-attention and convolutional layers [EB / OL] . (2019-11-08) [2022-09-11] . https:∥arxiv. org / abs/ 1911. 03584.
[14] GONG J J, QIU X P, CHEN X C, et al. Convolutional interaction network for natural language inference [ C] ∥ Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2018: 1576-1585.
[15] YANG G, ZHOU Y L, CHEN X, et al. Fine-grained pseudo-code generation method via code feature extraction and transformer[C]∥2021 28th Asia-Pacific Software Engineering Conference ( APSEC ) . Piscataway: IEEE, 2022: 213-222.
[16] 张安琳, 张启坤, 黄道颖, 等. 基于 CNN 与 BiGRU 融合神经网络的入侵检测模型[ J] . 郑州大学学报( 工学版) , 2022, 43(3) : 37-43.
ZHANG A L, ZHANG Q K, HUANG D Y, et al. Intrusion detection model based on CNN and BiGRU fused neural network [ J ] . Journal of Zhengzhou University (Engineering Science) , 2022, 43(3) : 37-43.
[17] YUAN Y H, HUANG L, GUO J Y, et al. OCNet: object context for semantic segmentation[ J] . International Journal of Computer Vision, 2021, 129(8) : 2375-2398.
[18] GEHRING J, AULI M, GRANGIER D, et al. Convolutional sequence to sequence learning[ C]∥Proceedings of the 34th International Conference on Machine Learning. New York: ACM, 2017: 1243-1252.

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Last Update: 2023-10-22