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This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning.
“Tree2Tree Neural Translation Model for Learning Source Code Changes”
Saikat Chakraborty, Miltiadis Allamanis and Baishakhi Ray · 1901
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“Maybe Deep Neural Networks are the Best Choice for Modeling Source Code”
Rafael-Michael Karampatsis and Charles Sutton · 1903
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“Stochastic estimation of the maximum of a regression function”
Jack Kiefer and Jacob Wolfowitz · 1952
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“Automatic repair of real bugs in java: A large-scale experiment on the defects4j dataset”
Matias Martinez et al · 1964
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“Long short-term memory”
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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“Bidirectional recurrent neural networks”
Mike Schuster and Kuldip Paliwal · 1997
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“The Rosetta Stone: the story of the decoding of hieroglyphics”
Robert Solé and Dominique Valbelle · 2001
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“An exploratory study of how developers seek, relate, and collect relevant information during software maintenance tasks”
Andrew Ko, Brad Myers, Michael Coblenz and Htet Aung · 2006
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“A Practical Guide for Using Statistical Tests to Assess Randomized Algorithms in Software Engineering”
Andrea Arcuri and Lionel Briand · 2011
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“A Systematic Study of Automated Program Repair: Fixing 55 out of 105 Bugs for $8 Each”
Claire Le, Michael Dewey-Vogt, Stephanie Forrest and Westley Weimer · 2012
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“GZoltar: An Eclipse Plug-in for Testing and Debugging”
José Campos, André Riboira, Alexandre Perez and Rui Abreu · 2012
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“Practical recommendations for gradient-based training of deep architectures”
Yoshua Bengio · 2012
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“Leveraging Program Equivalence for Adaptive Program Repair: Models and First Results”
Westley Weimer, Zachary. Fry and Stephanie Forrest · 2013
Earlier work this paper cites.
“Sequence to sequence learning with neural networks”
Ilya Sutskever, Oriol Vinyals and Quoc Le · 2014
Earlier work this paper cites.
“Defects4J: A database of existing faults to enable controlled testing studies for Java programs”
René Just, Darioush Jalali and Michael Ernst · 2014
Earlier work this paper cites.
“On the Properties of Neural Machine Translation: Encoder-Decoder Approaches”
KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau and Yoshua Bengio · 2014
Earlier work this paper cites.
“Learning phrase representations using RNN encoder-decoder for statistical machine translation”
Kyunghyun Cho et al · 2014
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“Neural machine translation by jointly learning to align and translate”
Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio · 2014
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“Addressing the rare word problem in neural machine translation”
Minh-Thang Luong et al · 2014
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“The Strength of Random Search on Automated Program Repair”
Yuhua Qi et al · 2014
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“An Analysis of Patch Plausibility and Correctness for Generate-and-validate Patch Generation Systems”
Zichao Qi, Fan Long, Sara Achour and Martin Rinard · 2015
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“Is the cure worse than the disease? overfitting in automated program repair”
Edward Smith, Earl Barr, Claire Le and Yuriy Brun · 2015
Cited alongside, same era.
“Six Challenges for Neural Machine Translation”
Philipp Koehn and Rebecca Knowles · 2017
Later among the works it cites.
“Dynamic Neural Program Embedding for Program Repair”
Ke Wang, Rishabh Singh and Zhendong Su · 2017
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“Context-Aware Patch Generation for Better Automated Program Repair”, 2018
Ming Wen et al · 2018
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“A survey of machine learning for big code and naturalness”
Miltiadis Allamanis, Earl Barr, Premkumar Devanbu and Charles Sutton · 2018
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“An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation”
Michele Tufano et al · 2018
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Abigail See, Peter. Liu and Christopher. Manning · 2017
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“An empirical study of fault localization families and their combinations”
Daming Zou et al · 2019
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“Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities”
Martin White et al · 2019
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