Fetching the paper…
Reading the bibliography…
Deep Neural Networks have been shown to succeed at a range of natural language tasks such as machine translation and text summarization.
Program synthesis by sketching
A. Solar-Lezama · 2008
Earlier work this paper cites.
On the naturalness of software
A. Hindle, E. T. Barr, Z. Su, M. Gabel, and P. Devanbu · 2012
Earlier work this paper cites.
A fast and simple algorithm for training neural probabilistic language models
A. Mnih and Y. W. Teh · 2012
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
Earlier work this paper cites.
Detecting and characterizing semantic inconsistencies in ported code
B. Ray, M. Kim, S. Person, and N. Rungta · 2013
Earlier work this paper cites.
Learning natural coding conventions
M. Allamanis, E. T. Barr, C. Bird, and C. Sutton · 2014
Earlier work this paper cites.
On the properties of neural machine translation: Encoder–decoder approaches
K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio · 2014
Earlier work this paper cites.
Structured generative models of natural source code
C. J. Maddison and D. Tarlow · 2014
Cited alongside, same era.
GloVe: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
Cited alongside, same era.
Suggesting accurate method and class names
M. Allamanis, E. T. Barr, C. Bird, and C. Sutton · 2015
Cited alongside, same era.
Automated software transplantation
E. T. Barr, M. Harman, Y. Jia, A. Marginean, and J. Petke · 2015
Cited alongside, same era.
Predicting program properties from Big Code
V. Raychev, M. Vechev, and A. Krause · 2015
Cited alongside, same era.
Improved semantic representations from tree-structured long short-term memory networks
K. S. Tai, R. Socher, and C. D. Manning · 2015
Cited alongside, same era.
A convolutional attention network for extreme summarization of source code
M. Allamanis, H. Peng, and C. Sutton · 2016
Later among the works it cites.
A study of Visual Studio usage in practice
S. Amann, S. Proksch, S. Nadi, and M. Mezini · 2016
Later among the works it cites.
Learning Python code suggestion with a sparse pointer network
A. Bhoopchand, T. Rocktäschel, E. Barr, and S. Riedel · 2016
Later among the works it cites.
PHOG: probabilistic model for code
P. Bielik, V. Raychev, and M. Vechev · 2016
Later among the works it cites.
Improving coreference resolution by learning entity-level distributed representations
K. Clark and C. D. Manning · 2016
Later among the works it cites.
Probabilistic model for code with decision trees
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
V. Raychev, P. Bielik, and M. Vechev · 2016
Later among the works it cites.