Fetching the paper…
Reading the bibliography…
Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description.
Three models for the description of language
Noam Chomsky. 1956 · 1956
Earlier work this paper cites.
Signature verification using a “siamese” time delay neural network. In Advances in neural information processing systems . Morgan-Kaufmann, Denver, Colorado, USA, 737–744
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1993 · 1993
Earlier work this paper cites.
Formal syntax and semantics of programming languages
Kenneth Slonneger and Barry L Kurtz. 1995 · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
The PageRank Citation Ranking: Bringing Order to the Web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
Earlier work this paper cites.
Links between perceptrons, MLPs and SVMs. In Proceedings of the Twenty-first International Conference on Machine learning . ACM, Banff, Alberta, Canada, 23
Ronan Collobert and Samy Bengio. 2004 · 2004
Earlier work this paper cites.
Using an information retrieval system to retrieve source code samples. In Proceedings of the 28th international conference on software engineering . ACM, Shanghai, China, 905–908
Renuka Sindhgatta. 2006 · 2006
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008 · 2008
Earlier work this paper cites.
Sourcerer: mining searching internet-scale software repositories
Erik Linstead, Sushil Bajracharya, Trung Ngo, Paul Rigor, Cristina Lopes, and Pierre Baldi. 2009 · 2009
Earlier work this paper cites.
Improving source code search with natural language phrasal representations of method signatures. In 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE Computer Society, Lawrence, KS, USA, 524–527
Emily Hill, Lori Pollock, and K Vijay-Shanker. 2011 · 2011
Earlier work this paper cites.
Portfolio: finding relevant functions and their usage. In Proceedings of the 33rd International Conference on Software Engineering . ACM, Waikiki, Honolulu , HI, USA, 111–120
Collin McMillan, Mark Grechanik, Denys Poshyvanyk, Qing Xie, and Chen Fu. 2011 · 2011
Earlier work this paper cites.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . ACL, Doha, Qatar, 1724–1734
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) . ACL, Doha, Qatar, 1746–1751
Yoon Kim. 2014 · 2014
Earlier work this paper cites.
The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations . ACL, Baltimore, MD, USA, 55–60
Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014 · 2014
Earlier work this paper cites.
GloVe: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) . ACL, Doha, Qatar, 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
Earlier work this paper cites.
Codehow: Effective code search based on api understanding and extended boolean model. In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE Computer Society, Lincoln, NE, USA, 260–270
Fei Lv, Hongyu Zhang, Jian-guang Lou, Shaowei Wang, Dongmei Zhang, and Jianjun Zhao. 2015 · 2015
Earlier work this paper cites.
Learning to generate pseudo-code from source code using statistical machine translation (t). In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE Computer Society, Lincoln, NE, USA, 574–584
Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. 2015 · 2015
Earlier work this paper cites.
Learning local feature descriptors with triplets and shallow convolutional neural networks. In Proceedings of the British Machine Vision Conference (BMVC) . BMVA Press, York, UK, 3
Vassileios Balntas, Edgar Riba, Daniel Ponsa, and Krystian Mikolajczyk. 2016 · 2016
Earlier work this paper cites.
Gated Graph Sequence Neural Networks. In 4th International Conference on Learning Representations , Yoshua Bengio and Yann LeCun (Eds.). OpenReview.net, San Juan, Puerto Rico
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016 · 2016
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Earlier work this paper cites.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017 · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning . PMLR, Sydney, NSW, Australia, 1263–1272
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems . Curran Associates, Inc., Long Beach, CA, USA, 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations . OpenReview.net, Toulon, France
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Attention is all you need. In Advances in neural information processing systems . Curran Associates, Inc., Long Beach, CA, USA, 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds . OpenReview.net, New Orleans, LA, USA
Matthias Fey and Jan E. Lenssen. 2019 · 2019
Later among the works it cites.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 2019
Later among the works it cites.
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (3rd draft ed.)
Daniel Jurafsky and James H. Martin. 2019 · 2019
Later among the works it cites.
Neural Code Search Evaluation Dataset
Hongyu Li, Seohyun Kim, and Satish Chandra. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A Compare-Aggregate Model for Matching Text Sequences. In International Conference on Learning Representations . OpenReview.net, Toulon, France
Shuohang Wang and Jing Jiang. 2017 · 2017
Cited alongside, same era.
Bilateral Multi-Perspective Matching for Natural Language Sentences. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 . ijcai.org, Melbourne, Australia, 4144–4150
Zhiguo Wang, Wael Hamza, and Radu Florian. 2017 · 2017
Cited alongside, same era.
A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton. 2018a · 2018
Cited alongside, same era.
Deep Code Search. In Proceedings of the 40th International Conference on Software Engineering . Association for Computing Machinery, Gothenburg, Sweden, 933–944
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018 · 2018
Cited alongside, same era.
Retrieval on source code: a neural code search. In Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages . ACM, Philadelphia, PA, USA, 31–41
Saksham Sachdev, Hongyu Li, Sifei Luan, Seohyun Kim, Koushik Sen, and Satish Chandra. 2018 · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks. In European Semantic Web Conference . Springer, Heraklion, Crete, Greece, 593–607
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Cited alongside, same era.
Graph Attention Networks. In 6th International Conference on Learning Representations . OpenReview.net, Vancouver, BC, Canada
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman. 2018 · 2018
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems . Curran Associates, Inc., Vancouver, BC, Canada, 8026–8037
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Later among the works it cites.
Multi-modal attention network learning for semantic source code retrieval. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, San Diego, CA, USA, 13–25
Yao Wan, Jingdong Shu, Yulei Sui, Guandong Xu, Zhou Zhao, Jian Wu, and Philip Yu. 2019 · 2019
Later among the works it cites.
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, Anchorage, AK, USA, 1418–1428
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia, and Charu Aggarwal. 2019 · 2019
Later among the works it cites.
How Powerful are Graph Neural Networks?. In 7th International Conference on Learning Representations . OpenReview.net, New Orleans, LA, USA
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Later among the works it cites.
CoaCor: code annotation for code retrieval with reinforcement learning. In The World Wide Web Conference (WWW) . ACM, San Francisco, CA, USA, 2203–2214
Ziyu Yao, Jayavardhan Reddy Peddamail, and Huan Sun. 2019 · 2019
Later among the works it cites.
Zhen Zhang, Yijian Xiang, Lingfei Wu, Bing Xue, and Arye Nehorai. 2019 · 2019
Later among the works it cites.
A Transformer-based Approach for Source Code Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL (Short) . ACL, Virtual Event, 4998–5007
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020 · 2020
Closest in time.
Structural language models of code. In Thirty-seventh International Conference on Machine Learning . PMLR, Virtual Event, 245–256
Uri Alon, Roy Sadaka, Omer Levy, and Eran Yahav. 2020 · 2020
Closest in time.
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings. In Advances in Neural Information Processing Systems . Curran Associates, Inc., Virtual Event
Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020 · 2020
Closest in time.
A Fair Comparison of Graph Neural Networks for Graph Classification. In International Conference on Learning Representations . OpenReview.net, Addis Ababa, Ethiopia
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli. 2020 · 2020
Closest in time.
A Multi-Perspective Architecture for Semantic Code Search. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . ACL, Virtual Event, 8563–8568
Rajarshi Haldar, Lingfei Wu, Jinjun Xiong, and Julia Hockenmaier. 2020 · 2020
Closest in time.
Improved code summarization via a graph neural network
Alexander LeClair, Sakib Haque, Linfgei Wu, and Collin McMillan. 2020 · 2020
Closest in time.
Hierarchical graph matching networks for deep graph similarity learning
Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X Liu, Chunming Wu, and Shouling Ji. 2020 · 2020
Closest in time.
Deep Graph Learning: Foundations, Advances and Applications. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, Virtual Event, 3555–3556
Yu Rong, Tingyang Xu, Junzhou Huang, Wenbing Huang, Hong Cheng, Yao Ma, Yiqi Wang, Tyler Derr, Lingfei Wu, and Tengfei Ma. 2020 · 2020
Closest in time.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2021 · 2021
Closest in time.
Summarizing source code using a neural attention model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . ACL, Berlin, Germany, 2073–2083
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
Closest in time.