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Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair.
Control flow analysis
Frances E. Allen · 1970
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Compilers: Principles, Techniques, and Tools (2nd Edition)
Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman · 2006
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Learning to execute, 2014
Wojciech Zaremba and Ilya Sutskever · 2014
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Learning to transduce with unbounded memory
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, and Phil Blunsom · 2015
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Inferring algorithmic patterns with stack-augmented recurrent nets
Armand Joulin and Tomas Mikolov · 2015
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Łukasz Kaiser and Ilya Sutskever · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Gated Graph Sequence Neural Networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Neural Programmer-Interpreters
Scott Reed and Nando de Freitas · 2015
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Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, and Demis Hassabis · 2016
Cited alongside, same era.
Making neural programming architectures generalize via recursion, 2017
Jonathon Cai, Richard Shin, and Dawn Song · 2017
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Differentiable programs with neural libraries
Alexander L Gaunt, Marc Brockschmidt, Nate Kushman, and Daniel Tarlow · 2017
Systematic generalization: What is required and can it be learned?
Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, and Aaron Courville · 2019
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Generative code modeling with graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, and Oleksandr Polozov · 2019
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Relational graph attention networks, 2019
Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y. Hammerla · 2019
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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 · 2019
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Inferring javascript types using graph neural networks, 2019
Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, and Liam Atkinson · 2019
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Learning to fix build errors with graph2diff neural networks, 2019
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Graph attention networks, 2017
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2018
Cited alongside, same era.
Neural-guided deductive search for real-time program synthesis from examples, 2018
Ashwin Kalyan, Abhishek Mohta, Oleksandr Polozov, Dhruv Batra, Prateek Jain, and Sumit Gulwani · 2018
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Learning loop invariants for program verification
Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, and Le Song · 2018
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Neural arithmetic logic units, 2018
Andrew Trask, Felix Hill, Scott Reed, Jack Rae, Chris Dyer, and Phil Blunsom · 2018
Cited alongside, same era.
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, and Edward Aftandilian · 2019
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Global relational models of source code
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2020
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Learning execution through neural code fusion
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, and Milad Hashemi · 2020
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Lambdanet: Probabilistic type inference using graph neural networks
Jiayi Wei, Maruth Goyal, Greg Durrett, and Isil Dillig · 2020
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