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Software vulnerabilities affect all businesses and research is being done to avoid, detect or repair them.
“On the naturalness of software”
Abram Hindle et al · 2012
Earlier work this paper cites.
“Estimating the global cost of cybercrime”
Net Losses · 2014
Earlier work this paper cites.
“Sequence to sequence learning with neural networks”
I Sutskever, O Vinyals and QV Le · 2014
Earlier work this paper cites.
“On the properties of neural machine translation: Encoder-decoder approaches”
Kyunghyun Cho, Bart Vanënboer, Dzmitry Bahdanau and Yoshua Bengio · 2014
Earlier work this paper cites.
“Neural machine translation of rare words with subword units”
Rico Sennrich, Barry Haddow and Alexandra Birch · 2015
Earlier work this paper cites.
“Mining Software Repair Models for Reasoning on the Search Space of Automated Program Fixing”
Matias Martinez and Martin Monperrus · 2015
Earlier work this paper cites.
“BovInspector: automatic inspection and repair of buffer overflow vulnerabilities”
Fengjuan Gao, Linzhang Wang and Xuandong Li · 2016
Cited alongside, same era.
“Vurle: Automatic vulnerability detection and repair by learning from examples”
Siqi Ma et al · 2017
Cited alongside, same era.
“Attention is all you need”
Ashish Vaswani et al · 2017
Cited alongside, same era.
“Are deep neural networks the best choice for modeling source code?”
Vincent Hellendoorn and Premkumar Devanbu · 2017
Cited alongside, same era.
“Learning to repair software vulnerabilities with generative adversarial networks”
Jacob Harer et al · 2018
Cited alongside, same era.
“Enabling the Continous Analysis of Security Vulnerabilities with VulData7”
Matthieu Jimenez, Mike Papadakis and Yves Traon · 2018
Cited alongside, same era.
“An empirical investigation into learning bug-fixing patches in the wild via neural machine translation.”
Michele Tufano et al · 2018
Later among the works it cites.
Taku Kudo and John Richardson · 2018
Later among the works it cites.
“Sequencer: Sequence-to-sequence learning for end-to-end program repair”
Zimin Chen et al · 2019
Closest in time.
“Maybe Deep Neural Networks are the Best Choice for Modeling Source Code”
Rafael-Michael Karampatsis and Charles Sutton · 2019
Closest in time.
“GH Archive” Accessed: 2019-11-27, http://www.gharchive.org/
2019
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“OpenNMT: Open-Source Toolkit for Neural Machine Translation”
G. Klein et al
Cited in the paper.