Neural Detection of Semantic Code Clones Via Tree-Based Convolution

Yu, Hao; Lam, Wing; Chen, Long; Li, Ge; Xie, Tao; Wang, Qianxiang

doi:10.1109/icpc.2019.00021

Cited by 110 publications

(86 citation statements)

References 19 publications

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“…In recent years, with the rapid development of deep learning technology, researchers in the field of software engineering begin to analyze the structural and semantic features of source code with deep learning approaches. Based on the common neural networks such as the convolutional neural network, the recursive neural network, and the recurrent neural network, scholars exploit various deep encoders to represent source code in different vector spaces [4][5][6]. Compared to traditional approaches, deep neural networks can extract the structural and semantic information hidden in the source code snippets during code embedding, which might improve the representation ability of code vectors.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] An Intelligent Code Search Approach Using Hybrid Encoders

Meng¹

2021

Wireless Communications and Mobile Computing

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The intelligent code search with natural language queries has become an important researching area in software engineering. In this paper, we propose a novel deep learning framework At-CodeSM for source code search. The powerful code encoder in At-CodeSM, which is implemented with an abstract syntax tree parsing algorithm (Tree-LSTM) and token-level encoders, maintains both the lexical and structural features of source code in the process of code vectorizing. Both the representative and discriminative models are implemented with deep neural networks. Our experiments on the CodeSearchNet dataset show that At-CodeSM yields better performance in the task of intelligent code searching than previous approaches.

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Section: Introductionmentioning

confidence: 99%

[Retracted] An Intelligent Code Search Approach Using Hybrid Encoders

Meng¹

2021

Wireless Communications and Mobile Computing

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“…Vast sources of code in open source repositories and forums make deep learning feasible for SE tasks. Code Summarization (Movshovitz-Attias and Cohen, 2013;Allamanis et al, 2016;Iyer et al, 2016;Alon et al, 2019a;Hu et al, 2018;Harer et al, 2019;Ahmad et al, 2020), Bug Detection (Ray et al, 2016;Li et al, 2018b;Russell et al, 2018;, Program Repair (Chen et al, 2019;Lutellier et al, 2020), Code Translation (Chen et al, 2018;Drissi et al, 2018;Xu et al, 2020), Clone Detection (Zhang et al, 2019;Yu et al, 2019;, Code completion (Li et al, 2018a;Hellendoorn and Devanbu, 2017;Parvez et al, 2018) are some of the tasks that are addressed with deep neural solution. While most of the prior approaches use task-specific representation learning, a few works (Alon et al, 2019b;Feng et al, 2020;Lachaux et al, 2020;Clement et al, 2020) attempted to learn transferable representations in an unsupervised fashion.…”

Section: Deep Learning In Software Engineeringmentioning

confidence: 99%

Unified Pre-training for Program Understanding and Generation

Ahmad¹,

Chakraborty²,

Ray³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

341

146

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Code summarization and generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence model capable of performing a broad spectrum of program and language understanding and generation tasks. PLBART is pre-trained on an extensive collection of Java and Python functions and associated NL text via denoising autoencoding. Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models. Moreover, experiments on discriminative tasks, e.g., program repair, clone detection, and vulnerable code detection, demonstrate PLBART's effectiveness in program understanding. Furthermore, analysis reveals that PLBART learns program syntax, style (e.g., identifier naming convention), logical flow (e.g., if block inside an else block is equivalent to else if block) that are crucial to program semantics and thus excels even with limited annotations.

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“…In the tool CCLearner, the frequencies of eight token categories in an AST and their similarity in clone pairs are used as features for supervised learning of a clone detector [20]. Yu et al use convolutional networks and combine structural information from ASTs with lexical information from tokens in their embedding [21]. AST embeddings are in particular studied in [22], using the cosine similarity of vectors representing code clones for evaluation.…”

Section: B Code Clone Detectionmentioning

confidence: 99%

Detection of Similar Functions Through the Use of Dominator Information

Schafer

Amme

Heinze

2020

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

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The detection of code clones is an important technique for finding malware and malicious code. Many existing methods work on source code to detect code clones. Recent work in this area has started to focus on the analysis of compiled code to find malicious code. We introduce a new method to detect code clones using Java bytecode and control flow information from dominator trees. A prototype implementation was developed and compared with the state-of-the-art clone detector NiCad to evaluate the basic functionality of the method. First experiments have shown that the method can reliably find Type 1 and Type 2 clones and even find additional clones.

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Neural Detection of Semantic Code Clones Via Tree-Based Convolution

Cited by 110 publications

References 19 publications

[Retracted] An Intelligent Code Search Approach Using Hybrid Encoders

[Retracted] An Intelligent Code Search Approach Using Hybrid Encoders

Unified Pre-training for Program Understanding and Generation

Detection of Similar Functions Through the Use of Dominator Information

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