Deep learning code fragments for code clone detection

By Martin White, Michele Tufano, Christopher Vendome, Denys Poshyvanyk. 2016

690 citations recorded by the source corpus.

Subfields: Information Systems, Software, Code Generation & Program Synthesis, Signal Processing

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Citations in the corpus

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation (2021, background): “• code-code (clone detection [65, 84, 46, 80, 9, 89, 86], defect detection [91, 55, 51, 42, 78, 79], cloze test [15], code completion [57, 1, 56, 66, 67, 8, 27, 7], code repair [69, 4, 23, 26, 71, 73], and code-to-code translation [48, 37, 10, 41]) • text-code (natural language code search [32, 20, ”
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair (2018, methodology): “DeepRepair leverages learned code similarities, captured with recursive autoencoders [50], to select repair ingredients from code fragments that are similar to the buggy code.”
GraphCodeBERT: Pre-training Code Representations with Data Flow (2020, background): “RtvNN (White et al., 2016) trains a recursive autoencoder to learn representations for AST.”
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, background): “Some examples of recent models that can be used in a number of software engineering tasks include: code completion [67, 91], defect prediction [84], bug localization [39], clone detection [80, 90], code search [23], learning Application Programming Interface (API) sequences [24], recommending method”
DeepBugs: a learning approach to name-based bug detection (2018, background): “Deep neural networks also help recommend API usages [Gu et al. 2016], detect code clones [White et al. 2016], classify programs into pre-defined categories [Mou et al. 2016], and adapt copied-and-pasted code to its surrounding code [Allamanis and Brockschmidt 2017].”
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code (2020, background): “…units is not limited to language modeling, but can easily be incorporated into any neural model of code, such as models to suggest readable names [3], summarizing source code [5, 49], predicting bugs [70], detecting code clones [87], comment generation [47], and variable de-obfuscation [10].”
Neural Code Comprehension: A Learnable Representation of Code Semantics (2018, methodology): “For the former task, code embeddings have been used to perform function or variable naming [2, 7], clone detection [63], code completion [54, 65], summarization [6], and algorithm classification [49].”
DeepSim: deep learning code functional similarity (2018, methodology): “We also compared DeepSim with the three other state-of-the-art approaches:DECKARD[26],RtvNN[58]andCDLH[57].”
Learning a classifier for false positive error reports emitted by static code analysis tools (2017, methodology): “…there have been many studies using statistical language models for handling important software development and maintenance tasks (Nguyen et al. 2013; Tu et al. 2014; Raychev et al. 2014, 2015; Allamanis et al. 2015, 2016; Fowkes and Sutton 2016; Gu et al. 2016; White et al. 2016; Dam et al. 2016).”
Contrastive Code Representation Learning (2020, methodology): “We address clone detection (White et al., 2016), type inference (Hellendoorn et al., 2018), and summarization (Alon et al., 2019a).”
Software Defect Prediction via Convolutional Neural Network (2017, methodology): “[43] proposed to model sequential software languages using deep learning and applied their models in the task of code clone detection.”
Are deep neural networks the best choice for modeling source code? (2017, methodology): “Count Models DL models are now used everywhere, including for modeling of source code [3, 37].”
HELoC: Hierarchical Contrastive Learning of Source Code Representation (2022, methodology): “Token-Based Models: RtvNN [45] uses an RNN to learn the representations of code tokens and trains a recursive autoencoder to learn AST representations.”
CoCoNuT: combining context-aware neural translation models using ensemble for program repair (2020, methodology): “Deep Learning in Software Engineering: The software engineering community had applied deep learning to perform various tasks such as defects prediction [40, 79, 81], source code representation [6, 7, 66, 80], source code summarization [4, 22] source code modeling [5, 11, 26, 83], code clone detectio”