Fetching the paper…
Reading the bibliography…
Code completion has become an essential component of integrated development environments.
Code Prediction by Feeding Trees to Transformers
Kim, S.; Zhao, J.; Tian, Y.; and Chandra, S. 2020 · 2003
Earlier work this paper cites.
Yang, Y. 2020 · 2003
Earlier work this paper cites.
Sequence Model Design for Code Completion in the Modern IDE
Aye, G. A.; and Kaiser, G. E. 2020 · 2004
Earlier work this paper cites.
Revisiting Multi-Task Learning in the Deep Learning Era
Vandenhende, S.; Georgoulis, S.; Proesmans, M.; Dai, D.; and Gool, L. V. 2020 · 2004
Earlier work this paper cites.
How Are Java Software Developers Using the Eclipse IDE?
Murphy, G. C.; Kersten, M.; and Findlater, L. 2006 · 2006
Earlier work this paper cites.
Learning from examples to improve code completion systems
Bruch, M.; Monperrus, M.; and Mezini, M. 2009 · 2009
Earlier work this paper cites.
On the naturalness of software
Hindle, A.; Barr, E. T.; Su, Z.; Gabel, M.; and Devanbu, P. T. 2012 · 2012
Earlier work this paper cites.
Code completion with statistical language models
Raychev, V.; Vechev, M. T.; and Yahav, E. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
Earlier work this paper cites.
Graph-Based Statistical Language Model for Code
Nguyen, A. T.; and Nguyen, T. N. 2015 · 2015
Earlier work this paper cites.
Intelligent Code Completion with Bayesian Networks
Proksch, S.; Lerch, J.; and Mezini, M. 2015 · 2015
Earlier work this paper cites.
Pointer Networks
Vinyals, O.; Fortunato, M.; and Jaitly, N. 2015 · 2015
Earlier work this paper cites.
Learning Python Code Suggestion with a Sparse Pointer Network
Bhoopchand, A.; Rocktäschel, T.; Barr, E. T.; and Riedel, S. 2016 · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Neural Code Completion
Liu, C.; Wang, X.; Shin, R.; Gonzalez, J. E.; and Song, D. 2016 · 2016
Cited alongside, same era.
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Mou, L.; Li, G.; Zhang, L.; Wang, T.; and Jin, Z. 2016 · 2016
Cited alongside, same era.
Automatically learning semantic features for defect prediction
Wang, S.; Liu, T.; and Tan, L. 2016 · 2016
Cited alongside, same era.
Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Graph Attention Networks
Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Liò, P.; and Bengio, Y. 2018 · 2018
Later among the works it cites.
Generative Code Modeling with Graphs
Brockschmidt, M.; Allamanis, M.; Gaunt, A. L.; and Polozov, O. 2019 · 2019
Later among the works it cites.
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context
Dai, Z.; Yang, Z.; Yang, Y.; Carbonell, J. G.; Le, Q. V.; and Salakhutdinov, R. 2019 · 2019
Later among the works it cites.
When code completion fails: a case study on real-world completions
Hellendoorn, V. J.; Proksch, S.; Gall, H. C.; and Bacchelli, A. 2019 · 2019
Later among the works it cites.
TreeCaps: Tree-Structured Capsule Networks for Program Source Code Processing
Jayasundara, V.; Bui, N. D. Q.; Jiang, L.; and Lo, D. 2019 · 2019
Later among the works it cites.
Pythia: AI-assisted Code Completion System
Svyatkovskiy, A.; Zhao, Y.; Fu, S.; and Sundaresan, N. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A Survey of Machine Learning for Big Code and Naturalness
Allamanis, M.; Barr, E. T.; Devanbu, P. T.; and Sutton, C. A. 2018 · 2018
Cited alongside, same era.
Learning to Represent Programs with Graphs
Allamanis, M.; Brockschmidt, M.; and Khademi, M. 2018 · 2018
Cited alongside, same era.
A general path-based representation for predicting program properties
Alon, U.; Zilberstein, M.; Levy, O.; and Yahav, E. 2018 · 2018
Cited alongside, same era.
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Chen, Z.; Badrinarayanan, V.; Lee, C.; and Rabinovich, A. 2018 · 2018
Cited alongside, same era.
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Kendall, A.; Gal, Y.; and Cipolla, R. 2018 · 2018
Cited alongside, same era.
Code Completion with Neural Attention and Pointer Networks
Li, J.; Wang, Y.; Lyu, M. R.; and King, I. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
A novel neural source code representation based on abstract syntax tree
Zhang, J.; Wang, X.; Zhang, H.; Sun, H.; Wang, K.; and Liu, X. 2019 · 2019
Later among the works it cites.
Deep Learning for Source Code Modeling and Generation: Models, Applications, and Challenges
Le, T. H. M.; Chen, H.; and Babar, M. A. 2020 · 2020
Later among the works it cites.
Improved Code Summarization via a Graph Neural Network
LeClair, A.; Haque, S.; Wu, L.; and McMillan, C. 2020 · 2020
Later among the works it cites.
A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning
Liu, F.; Li, G.; Wei, B.; Xia, X.; Fu, Z.; and Jin, Z. 2020 · 2020
Later among the works it cites.
A Comprehensive Survey on Graph Neural Networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Yu, P. S. 2020 · 2020
Later among the works it cites.