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We address the problem of predicting edit completions based on a learned model that was trained on past edits.
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Suggesting Accurate Method and Class Names. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (Bergamo, Italy) (ESEC/FSE 2015) . ACM, New York, NY, USA, 38–49
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The Adverse Effects of Code Duplication in Machine Learning Models of Code. In Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Athens, Greece) (Onward! 2019) . Association for Computing Machinery, New York, NY, USA, 143–153
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End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 1064–1074
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Attention is All you Need
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A general path-based representation for predicting program properties
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Neural Program Repair by Jointly Learning to Localize and Repair. In International Conference on Learning Representations
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Learning to Represent Edits. In International Conference on Learning Representations
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HOPPITY: LEARNING GRAPH TRANSFORMATIONS TO DETECT AND FIX BUGS IN PROGRAMS. In International Conference on Learning Representations
Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang. 2020 · 2020
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Global Relational Models of Source Code. In International Conference on Learning Representations
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber. 2020 · 2020
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Towards String-To-Tree Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Association for Computational Linguistics, Vancouver, Canada, 132–140
Roee Aharoni and Yoav Goldberg. 2017 · 2021
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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) . Association for Computational Linguistics, Berlin, Germany, 2073–2083
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016 · 2083
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A Convolutional Attention Network for Extreme Summarization of Source Code. In Proceedings of The 33rd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 48) , Maria Florina Balcan and Kilian Q. Weinberger (Eds.). PMLR, New York, New York, USA, 2091–2100
Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016 · 2091
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