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We propose a method combining machine learning with a static analysis tool (i.e.
Bears: An Extensible Java Bug Benchmark for Automatic Program Repair Studies
Fernanda Madeiral, Simon Urli, Marcelo Maia, and Martin Monperrus · 1901
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Extracting and composing robust features with denoising autoencoders
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Automatically patching errors in deployed software
Jeff H. Perkins, Sunghun Kim, Samuel Larsen, Saman P. Amarasinghe, Jonathan Bachrach, Michael Carbin, Carlos Pacheco, Frank Sherwood, Stelios Sidiroglou, Gregory T. Sullivan, Weng-Fai Wong, Yoav Zibin, Michael D. Ernst, and Martin C. Rinard · 2009
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Modular and verified automatic program repair
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Defects4j: A database of existing faults to enable controlled testing studies for java programs
René Just, Darioush Jalali, and Michael D Ernst · 2014
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2015
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Introclassjava: A benchmark of 297 small and buggy java programs
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Where is the bug and how is it fixed? an experiment with practitioners
Marcel Böhme, Ezekiel Olamide Soremekun, Sudipta Chattopadhyay, Emamurho Ugherughe, and Andreas Zeller · 2017
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Deepfix: Fixing common c language errors by deep learning
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Codeflaws: a programming competition benchmark for evaluating automated program repair tools
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Semantic equivalence checking for HHVM bytecode
Nick Benton · 2018
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Neuro-symbolic program corrector for introductory programming assignments
Sahil Bhatia, Pushmeet Kohli, and Rishabh Singh · 2018
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Deepbugs: A learning approach to name-based bug detection
Michael Pradel and Koushik Sen · 2018
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Bugs.jar: A large-scale, diverse dataset of real-world java bugs
Ripon K Saha, Yingjun Lyu, Wing Lam, Hiroaki Yoshida, and Mukul R Prasad · 2018
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Getafix: Learning to fix bugs automatically
Johannes Bader, Andrew Scott, Michael Pradel, and Satish Chandra · 2019
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Sequencer: Sequence-to-sequence learning for end-to-end program repair
Zimin Chen, Steve James Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Martin Monperrus · 2019
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Scaling static analyses at facebook
Dino Distefano, Manuel Fähndrich, Francesco Logozzo, and Peter W. O’Hearn · 2019
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Reducing transformer depth on demand with structured dropout
Global relational models of source code
Vincent J Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2020
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Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
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Graph-based, self-supervised program repair from diagnostic feedback
Michihiro Yasunaga and Percy Liang · 2020
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Unified pre-training for program understanding and generation
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Self-supervised bug detection and repair
Miltiadis Allamanis, Henry Jackson-Flux, and Marc Brockschmidt · 2021
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Deepdebug: Fixing python bugs using stack traces, backtranslation, and code skeletons
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Angela Fan, Edouard Grave, and Armand Joulin · 2019
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Coda: An end-to-end neural program decompiler
Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, and Jishen Zhao · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2019
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Re-factoring based program repair applied to programming assignments
Yang Hu, Umair Z Ahmed, Sergey Mechtaev, Ben Leong, and Abhik Roychoudhury · 2019
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Omer Katz, Yuval Olshaker, Yoav Goldberg, and Eran Yahav · 2019
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Avatar: Fixing semantic bugs with fix patterns of static analysis violations
Kui Liu, Anil Koyuncu, Dongsun Kim, and Tegawendé F Bissyandé · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk · 2019
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Dawn Drain, Colin B Clement, Guillermo Serrato, and Neel Sundaresan · 2021
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DOBF: A deobfuscation pre-training objective for programming languages
Marie-Anne Lachaux, Baptiste Roziere, Marc Szafraniec, and Guillaume Lample · 2021
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Semantics-recovering decompilation through neural machine translation
Ruigang Liang, Ying Cao, Peiwei Hu, Jinwen He, and Kai Chen · 2021
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Automatic program repair with openai’s codex: Evaluating quixbugs
Julian Aron Prenner and Romain Robbes · 2021
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Project CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks
Ruchir Puri, David S. Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir R. Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, and Ulrich Finkler · 2021
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Vul4j: a dataset of reproducible java vulnerabilities geared towards the study of program repair techniques
Quang-Cuong Bui, Riccardo Scandariato, and Nicolás E Díaz Ferreyra · 2022
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Palm: Scaling language modeling with pathways
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Patch generation with language models: Feasibility and scaling behavior
Sophia D Kolak, Ruben Martins, Claire Le Goues, and Vincent Josua Hellendoorn · 2022
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Code translation with compiler representations
Marc Szafraniec, Baptiste Roziere, Hugh Leather Francois Charton, Patrick Labatut, and Gabriel Synnaeve · 2022
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Selfapr: Self-supervised program repair with test execution diagnostics
He Ye, Matias Martinez, Xiapu Luo, Tao Zhang, and Martin Monperrus · 2022
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