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Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data.
On finding lowest common ancestors in trees
Alfred V Aho, John E Hopcroft, and Jeffrey D Ullman · 1976
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
A primer on kernel methods
Jean-Philippe Vert, Koji Tsuda, and Bernhard Schölkopf · 2004
Earlier work this paper cites.
Shortest-path kernels on graphs
Karsten M Borgwardt and Hans-Peter Kriegel · 2005
Earlier work this paper cites.
Cross-domain sentiment classification via spectral feature alignment
Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen · 2010
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
srcml: An infrastructure for the exploration, analysis, and manipulation of source code: A tool demonstration
Michael L Collard, Michael John Decker, and Jonathan I Maletic · 2013
Earlier work this paper cites.
Exploiting similarities among languages for machine translation
Tomas Mikolov, Quoc V Le, and Ilya Sutskever · 2013
Earlier work this paper cites.
Transfer defect learning
Jaechang Nam, Sinno Jialin Pan, and Sunghun Kim · 2013
Earlier work this paper cites.
Improving vector space word representations using multilingual correlation
Manaal Faruqui and Chris Dyer · 2014
Earlier work this paper cites.
An empirical study on the impact of static typing on software maintainability
Stefan Hanenberg, Sebastian Kleinschmager, Romain Robbes, Éric Tanter, and Andreas Stefik · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Predicting program properties from" big code"
Veselin Raychev, Martin Vechev, and Andreas Krause · 2015
Earlier work this paper cites.
A convolutional attention network for extreme summarization of source code
Miltiadis Allamanis, Hao Peng, and Charles Sutton · 2016
Earlier work this paper cites.
Deep learning code fragments for code clone detection
Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk · 2016
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To type or not to type: quantifying detectable bugs in javascript
Zheng Gao, Christian Bird, and Earl T Barr · 2017
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Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code
Huihui Wei and Ming Li · 2017
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Robust multilingual part-of-speech tagging via adversarial training
Michihiro Yasunaga, Jungo Kasai, and Dragomir Radev · 2017
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Adversarial deep averaging networks for cross-lingual sentiment classification
Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, and Kilian Weinberger · 2018
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Deephunter: a coverage-guided fuzz testing framework for deep neural networks
Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Minhui Xue, Hongxu Chen, Yang Liu, Jianjun Zhao, Bo Li, Jianxiong Yin, and Simon See · 2019
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Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu · 2019
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Compilers: principles, techniques and tools
Alfred V Aho, Monica S Lam, Ravi Sethi, and Jeffrey D Ullman · 2020
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Typilus: neural type hints
Miltiadis Allamanis, Earl T Barr, Soline Ducousso, and Zheng Gao · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Deep code search
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim · 2018
Cited alongside, same era.
Deep learning type inference
Vincent J Hellendoorn, Christian Bird, Earl T Barr, and Miltiadis Allamanis · 2018
Cited alongside, same era.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
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What’s in a domain? learning domain-robust text representations using adversarial training
Yitong Li, Timothy Baldwin, and Trevor Cohn · 2018
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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2018
Cited alongside, same era.
code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2019
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Unsupervised translation of programming languages
Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot, and Guillaume Lample · 2020
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Typewriter: Neural type prediction with search-based validation
Michael Pradel, Georgios Gousios, Jason Liu, and Satish Chandra · 2020
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Neural unsupervised domain adaptation in nlp—a survey
Alan Ramponi and Barbara Plank · 2020
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Lambdanet: Probabilistic type inference using graph neural networks
Jiayi Wei, Maruth Goyal, Greg Durrett, and Isil Dillig · 2020
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Adversarial examples for models of code
Noam Yefet, Uri Alon, and Eran Yahav · 2020
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Retrieval-based neural source code summarization
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu · 2020
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Learning type annotation: is big data enough?
Kevin Jesse, Premkumar T Devanbu, and Toufique Ahmed · 2021
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Retrieval-augmented generation for code summarization via hybrid GNN
Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu · 2021
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Rnnrepair: Automatic rnn repair via model-based analysis
Xiaofei Xie, Wenbo Guo, Lei Ma, Wei Le, Jian Wang, Lingjun Zhou, Yang Liu, and Xinyu Xing · 2021
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Spi: Automated identification of security patches via commits
Yaqin Zhou, Jing Kai Siow, Chenyu Wang, Shangqing Liu, and Yang Liu · 2021
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https://sites.google.com/view/cltl4sti/home , 2022
cltl4sti · 2022
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