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There have been multiple recent proposals on using deep neural networks for code search using natural language.
Term-weighting approaches in automatic text retrieval
Gerard Salton and Christopher Buckley · 1988
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Using the cosine measure in a neural network for document retrieval
Ross Wilkinson and Philip Hingston · 1991
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Information retrieval: Data structures & algorithms
William Bruce Frakes and Ricardo Baeza-Yates · 1992
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Effective software fault localization using an rbf neural network
W Eric Wong, Vidroha Debroy, Richard Golden, Xiaofeng Xu, and Bhavani Thuraisingham · 2011
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 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
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Bimodal modelling of source code and natural language
Miltos Allamanis, Daniel Tarlow, Andrew Gordon, and Yi Wei · 2015
Earlier work this paper cites.
Deepcoder: Learning to write programs
Matej Balog, Alexander L Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2016
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Learning unified features from natural and programming languages for locating buggy source code
Xuan Huo, Ming Li, and Zhi-Hua Zhou · 2016
Earlier work this paper cites.
Summarizing source code using a neural attention model
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer · 2016
Cited alongside, same era.
Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2017
Cited alongside, same era.
Learning bilingual word embeddings with (almost) no bilingual data
Mikel Artetxe, Gorka Labaka, and Eneko Agirre · 2017
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
Cited alongside, same era.
Word translation without parallel data
Alexis Conneau, Guillaume Lample, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou · 2017
Cited alongside, same era.
Deep code search
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim · 2018
Later among the works it cites.
Deep code search github repository, 2018
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim · 2018
Later among the works it cites.
How to create natural language semantic search for arbitrary objects with deep learning, 2018
Hamel Husain and Ho-Hsiang Wu · 2018
Later among the works it cites.
Towards natural language semantic code search, 2018
Hamel Husain and Ho-Hsiang Wu · 2018
Later among the works it cites.
Aroma: Code recommendation via structural code search
Sifei Luan, Di Yang, Koushik Sen, and Satish Chandra · 2018
Later among the works it cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez Melis and Tommi Jaakkola · 2018
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Semantically enhanced software traceability using deep learning techniques
Jin Guo, Jinghui Cheng, and Jane Cleland-Huang · 2017
Cited alongside, same era.
Bug localization with combination of deep learning and information retrieval
An Ngoc Lam, Anh Tuan Nguyen, Hoan Anh Nguyen, and Tien N Nguyen · 2017
Cited alongside, same era.
Regularizing and Optimizing LSTM Language Models
Stephen Merity, Nitish Shirish Keskar, and Richard Socher · 2017
Cited alongside, same era.
Bayesian sketch learning for program synthesis
Vijayaraghavan Murali, Swarat Chaudhuri, and Chris Jermaine · 2017
Cited alongside, same era.
code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2018
Cited alongside, same era.
A deep tree-based model for software defect prediction
Hoa Khanh Dam, Trang Pham, Shien Wee Ng, Truyen Tran, John Grundy, Aditya Ghose, Taeksu Kim, and Chul-Joo Kim · 2018
Cited alongside, same era.
Unsupervised alignment of embeddings with wasserstein procrustes
Edouard Grave, Armand Joulin, and Quentin Berthet · 2018
Cited alongside, same era.
Later among the works it cites.
Retrieval on source code: a neural code search
Saksham Sachdev, Hongyu Li, Sifei Luan, Seohyun Kim, Koushik Sen, and Satish Chandra · 2018
Later among the works it cites.
datastack exchange data dump, 2018
Inc. Stack Exchange · 2018
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Interpretable lstms for whole-brain neuroimaging analyses
Armin W Thomas, Hauke R Heekeren, Klaus-Robert Müller, and Wojciech Samek · 2018
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Execution-guided neural program decoding
Chenglong Wang, Po-Sen Huang, Alex Polozov, Marc Brockschmidt, and Rishabh Singh · 2018
Later among the works it cites.
Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
Later among the works it cites.
On learning meaningful code changes via neural machine translation
Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, and Denys Poshyvanyk · 2019
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