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Neural program embedding can be helpful in analyzing large software, a task that is challenging for traditional logic-based program analyses due to their limited scalability.
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Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller · 1905
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Beware the march of this ide: Eclipse is overshadowing other tool technologies
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Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman · 2006
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Deckard: Scalable and accurate tree-based detection of code clones
Lingxiao Jiang, Ghassan Misherghi, Zhendong Su, and Stephane Glondu · 2007
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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On the naturalness of software
Abram Hindle, Earl T Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu · 2012
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Phog: probabilistic model for code
Pavol Bielik, Veselin Raychev, and Martin Vechev · 2016
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin · 2016
Deepfix: Fixing common c language errors by deep learning
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade · 2017
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code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2018
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Code vectors: Understanding programs through embedded abstracted symbolic traces
Jordan Henkel, Shuvendu Lahiri, Ben Liblit, and Thomas Reps · 2018
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Path-based function embeddings
Daniel DeFreez, Aditya V. Thakur, and Cindy Rubio-González · 2018
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Search, align, and repair: Data-driven feedback generation for introductory programming exercises
Ke Wang, Rishabh Singh, and Zhendong Su · 2018
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2017
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