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Source code spends most of its time in a broken or incomplete state during software development.
Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints
Patrick Cousot and Radhia Cousot · 1977
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Leon Moonen · 2001
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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, and Ming Zhou · 2002
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Lightweight impact analysis using island grammars
Leon Moonen · 2002
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Principles of program analysis
Flemming Nielson, Hanne R Nielson, and Chris Hankin · 2004
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Practical scope recovery using bridge parsing
Emma Nilsson-Nyman, Torbjörn Ekman, and Görel Hedin · 2008
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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, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou · 2009
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On the naturalness of software
Abram Hindle, Earl T Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu · 2012
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Neural networks for machine learning
Geoffrey Hinton, Nitsh Srivastava, and Kevin Swersky · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Structured generative models of natural source code
Chris Maddison and Daniel Tarlow · 2014
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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Graph-based statistical language model for code
Anh Tuan Nguyen and Tien N Nguyen · 2015
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Predicting program properties from" big code"
Veselin Raychev, Martin Vechev, and Andreas Krause · 2015
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Tricorder: Building a program analysis ecosystem
Caitlin Sadowski, Jeffrey Van Gogh, Ciera Jaspan, Emma Soderberg, and Collin Winter · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Pattern-based vulnerability discovery
Fabian Yamaguchi · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Towards a definition of knowledge graphs
Lisa Ehrlinger and Wolfram Wöß · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin · 2016
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Universal dependencies v1: A multilingual treebank collection
Joakim Nivre, Marie-Catherine De Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajic, Christopher D Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, et al · 2016
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Neuro-symbolic program synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2016
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On the" naturalness" of buggy code
Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, and Premkumar Devanbu · 2016
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Global relational models of source code
Vincent J Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2019
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Maybe deep neural networks are the best choice for modeling source code
Rafael-Michael Karampatsis and Charles Sutton · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Novel positional encodings to enable tree-based transformers
Vighnesh Shiv and Chris Quirk · 2019
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Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson · 2019
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2017
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Deepfix: Fixing common c language errors by deep learning
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade · 2017
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Are deep neural networks the best choice for modeling source code?
Vincent J Hellendoorn and Premkumar Devanbu · 2017
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Learning graphical state transitions
Daniel D Johnson · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Abstract syntax networks for code generation and semantic parsing
Maxim Rabinovich, Mitchell Stern, and Dan Klein · 2017
Cited alongside, same era.
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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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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A novel neural source code representation based on abstract syntax tree
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, and Xudong Liu · 2019
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Structural language models of code
Uri Alon, Roy Sadaka, Omer Levy, and Eran Yahav · 2020
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Learning to execute programs with instruction pointer attention graph neural networks
David Bieber, Charles Sutton, Hugo Larochelle, and Daniel Tarlow · 2020
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Programl: Graph-based deep learning for program optimization and analysis
Chris Cummins, Zacharias V Fisches, Tal Ben-Nun, Torsten Hoefler, and Hugh Leather · 2020
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Hoppity: Learning graph transformations to detect and fix bugs in programs
Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang · 2020
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Learning graph structure with a finite-state automaton layer
Daniel D Johnson, Hugo Larochelle, and Daniel Tarlow · 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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Big code!= big vocabulary: Open-vocabulary models for source code
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, and Andrea Janes · 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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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2020
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Universal dependencies v2: An evergrowing multilingual treebank collection
Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Jan Hajič, Christopher D Manning, Sampo Pyysalo, Sebastian Schuster, Francis Tyers, and Daniel Zeman · 2020
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Gradient estimation with stochastic softmax tricks
Max B Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, and Chris J Maddison · 2020
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Intellicode compose: Code generation using transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 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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Pointer graph networks
Petar Velickovic, Lars Buesing, Matthew C Overlan, Razvan Pascanu, Oriol Vinyals, and Charles Blundell · 2020
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Towards full-line code completion with neural language models
Wenhan Wang, Sijie Shen, Ge Li, and Zhi Jin · 2020
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tie-Yan Liu · 2020
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Graph-based, self-supervised program repair from diagnostic feedback
Michihiro Yasunaga and Percy Liang · 2020
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https://github.com/joernio/joern
Joern: Open-source code analysis platform for c/c++/java based on code property graphs · 2021
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Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes, and Burkhard Rost · 2021
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Language-agnostic representation learning of source code from structure and context
Daniel Zügner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, and Stephan Günnemann · 2021
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