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Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks.
An investigation of procedure and variable names as beacons during program comprehension
Edward M Gellenbeck and Curtis R Cook · 1991
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The effects of comments and identifier names on program comprehensibility: an experimental investigation
Armstrong A Takang, Penny A Grubb, and Robert D Macredie · 1996
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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What’s in a name? a study of identifiers
Dawn Lawrie, Christopher Morrell, Henry Feild, and David Binkley · 2006
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Cognitive perspectives on the role of naming in computer programs
Ben Liblit, Andrew Begel, and Eve Sweetser · 2006
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Relating identifier naming flaws and code quality: An empirical study
Simon Butler, Michel Wermelinger, Yijun Yu, and Helen Sharp · 2009
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Learning natural coding conventions
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Suggesting accurate method and class names
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2015
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Predicting program properties from" big code"
Veselin Raychev, Martin Vechev, and Andreas Krause · 2015
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A convolutional attention network for extreme summarization of source code
Miltiadis Allamanis, Hao Peng, and Charles Sutton · 2016
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Statistical deobfuscation of android applications
Benjamin Bichsel, Veselin Raychev, Petar Tsankov, and Martin Vechev · 2016
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Déjàvu: a map of code duplicates on github
Cristina V Lopes, Petr Maj, Pedro Martins, Vaibhav Saini, Di Yang, Jakub Zitny, Hitesh Sajnani, and Jan Vitek · 2017
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Recovering clear, natural identifiers from obfuscated js names
Bogdan Vasilescu, Casey Casalnuovo, and Premkumar Devanbu · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Dynamic neural program embedding for program repair
Ke Wang, Rishabh Singh, and Zhendong Su · 2017
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Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code
Huihui Wei and Ming Li · 2017
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and M. Khademi · 2018
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A general path-based representation for predicting program properties
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2018
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Context2name: A deep learning-based approach to infer natural variable names from usage contexts
Rohan Bavishi, Michael Pradel, and Koushik Sen · 2018
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Tree-to-tree neural networks for program translation
Xinyun Chen, Chang Liu, and Dawn Song · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Deep code search
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim · 2018
Cited alongside, same era.
Deep code comment generation
Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le · 2019
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
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Neural reverse engineering of stripped binaries using augmented control flow graphs
Yaniv David, Uri Alon, and Eran Yahav · 2020
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Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2018
Cited alongside, same era.
Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R Lyu, and Irwin King · 2018
Cited alongside, same era.
A systematic review on code clone detection
Qurat Ul Ain, Wasi Haider Butt, Muhammad Waseem Anwar, Farooque Azam, and Bilal Maqbool · 2019
Cited alongside, same era.
The adverse effects of code duplication in machine learning models of code
Miltiadis Allamanis · 2019
Cited alongside, same era.
When deep learning met code search
Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra · 2019
Cited alongside, same era.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Zimin Chen, Steve James Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Martin Monperrus · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon · 2019
Cited alongside, same era.
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Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
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Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
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Contrastive code representation learning
Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph E Gonzalez, and Ion Stoica · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy · 2020
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Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
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Code prediction by feeding trees to transformers
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra · 2020
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A self-attentional neural architecture for code completion with multi-task learning
Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi Fu, and Zhi Jin · 2020
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Industry-scale ir-based bug localization: A perspective from facebook
Vijayaraghavan Murali, Lee Gross, Rebecca Qian, and Satish Chandra · 2020
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Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
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Fast and memory-efficient neural code completion
Alexey Svyatkovskoy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Franco, and Miltiadis Allamanis · 2020
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Learning to fix build errors with graph2diff neural networks
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, and Edward Aftandilian · 2020
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Detecting code clones with graph neural network and flow-augmented abstract syntax tree
Wenhan Wang, Ge Li, Bo Ma, Xin Xia, and Zhi Jin · 2020
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Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
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