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Automatic repair of real bugs in Java: a large-scale experiment on the Defects4J dataset
Martinez, M., Durieux, T., Sommerard, R., Xuan, J., and Monperrus, M · 1964
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Long short-term memory
Hochreiter, S., and Schmidhuber, J · 1997
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Populating a release history database from version control and bug tracking systems
Fischer, M., Pinzger, M., and Gall, H. C · 2003
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Modernizing Legacy Systems: Software Technologies, Engineering Process and Business Practices
seacord, R. C., Plakosh, D., and Lewis, G. A · 2003
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A systematic review of software development cost estimation studies
Jorgensen, M., and Shepperd, M · 2007
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How long will it take to fix this bug?
Weiss, C., Premraj, R., Zimmermann, T., and Zeller, A · 2007
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What’s a typical commit? A characterization of open source software repositories
Alali, A., Kagdi, H. H., and Maletic, J. I · 2008
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Is it a bug or an enhancement?: a text-based approach to classify change requests
Antoniol, G., Ayari, K., Di Penta, M., Khomh, F., and Guéhéneuc, Y · 2008
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A novel co-evolutionary approach to automatic software bug fixing
Arcuri, A., and Yao, X · 2008
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NICAD: accurate detection of near-miss intentional clones using flexible pretty-printing and code normalization
Roy, C. K., and Cordy, J. R · 2008
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Automatically patching errors in deployed software
Perkins, J. H., Kim, S., Larsen, S., Amarasinghe, S., Bachrach, J., Carbin, M., Pacheco, C., Sherwood, F., Sidiroglou, S., Sullivan, G., Wong, W.-F., Zibin, Y., Ernst, M. D., and Rinard, M · 2009
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An empirical exploration of regularities in open-source software lexicons
Pierret, D., and Poshyvanyk, D · 2009
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Automatically finding patches using genetic programming
Weimer, W., Nguyen, T., Le Goues, C., and Forrest, S · 2009
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A study of the uniqueness of source code
Gabel, M., and Su, Z · 2010
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GitHub Compare API
GitHub · 2010
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Statistical Machine Translation
Koehn, P · 2010
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Mining API mapping for language migration
Zhong, H., Thummalapenta, S., Xie, T., Zhang, L., and Wang, Q · 2010
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Automated atomicity-violation fixing
Jin, G., Song, L., Zhang, W., Lu, S., and Liblit, B · 2011
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Ll(*): The foundation of the ANTLR parser generator
Parr, T., and Fisher, K · 2011
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Sequence transduction with recurrent neural networks
Graves, A · 2012
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GitHub Archive
Grigorik, I · 2012
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Bug prediction based on fine-grained module histories
Hata, H., Mizuno, O., and Kikuno, T · 2012
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On the naturalness of software
Hindle, A., Barr, E. T., Su, Z., Gabel, M., and Devanbu, P · 2012
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A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each
Le Goues, C., Dewey-Vogt, M., Forrest, S., and Weimer, W · 2012
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Genprog: A generic method for automatic software repair
Le Goues, C., Nguyen, T., Forrest, S., and Weimer, W · 2012
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CVS-Vintage: A Dataset of 14 CVS Repositories of Java Software
Monperrus, M., and Martinez, M · 2012
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Zhou, J., Zhang, H., and Lo, D · 2012
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Audio chord recognition with recurrent neural networks
Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P · 2013
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Automatic recovery from runtime failures
Carzaniga, A., Gorla, A., Mattavelli, A., Perino, N., and Pezzè, M · 2013
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It’s not a bug, it’s a feature: how misclassification impacts bug prediction
Herzig, K., Just, S., and Zeller, A · 2013
Cited alongside, same era.
Recurrent continuous translation models
Kalchbrenner, N., and Blunsom, P · 2013
Cited alongside, same era.
Automatic patch generation learned from human-written patches
Kim, D., Nam, J., Song, J., and Kim, S · 2013
Cited alongside, same era.
A model of the commit size distribution of open source
Kolassa, C., Riehle, D., and Salim, M. A · 2013
Cited alongside, same era.
Lexical statistical machine translation for language migration
Nguyen, A. T., Nguyen, T. T., and Nguyen, T. N · 2013
Cited alongside, same era.
A study of repetitiveness of code changes in software evolution
Nguyen, H. A., Nguyen, A. T., Nguyen, T. T., Nguyen, T. N., and Rajan, H · 2013
Cited alongside, same era.
Watch out for this commit! A study of influential software changes
Li, D., Li, L., Kim, D., Bissyandé, T. F., Lo, D., and Traon, Y. L · 2016
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Automatic patch generation by learning correct code
Long, F., and Rinard, M · 2016
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Sk_p: A neural program corrector for MOOCs
Pu, Y., Narasimhan, K., Solar-Lezama, A., and Barzilay, R · 2016
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SourcererCC: Scaling code clone detection to big-code
Sajnani, H., Saini, V., Svajlenko, J., Roy, C. K., and Lopes, C. V · 2016
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From aristotle to ringelmann: a large-scale analysis of team productivity and coordination in open source software projects
Scholtes, I., Mavrodiev, P., and Schweitzer, F · 2016
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Semfix: Program repair via semantic analysis
Nguyen, H. D. T., Qi, D., Roychoudhury, A., and Chandra, S · 2013
Cited alongside, same era.
The Definitive ANTLR 4 Reference
Parr, T · 2013
Cited alongside, same era.
Leveraging program equivalence for adaptive program repair: Models and first results
Weimer, W., Fry, Z. P., and Forrest, S · 2013
Cited alongside, same era.
Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2014
Cited alongside, same era.
The plastic surgery hypothesis
Barr, E. T., Brun, Y., Devanbu, P., Harman, M., and Sarro, F · 2014
Cited alongside, same era.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gülçehre, Ç., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Cited alongside, same era.
Wang, S., Liu, T., and Tan, L · 2016
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Deep learning code fragments for code clone detection
White, M., Tufano, M., Vendome, C., and Poshyvanyk, D · 2016
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Nopol: Automatic repair of conditional statement bugs in Java programs
Xuan, J., Martínez, M., DeMarco, F., Clément, M., Lamelas, S., Durieux, T., Le Berre, D., and Monperrus, M · 2016
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Massive exploration of neural machine translation architectures
Britz, D., Goldie, A., Luong, M., and Le, Q. V · 2017
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The care and feeding of wild-caught mutants
Brown, D. B., Vaughn, M., Liblit, B., and Reps, T · 2017
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DeepAM: migrate APIs with multi-modal sequence to sequence learning
Gu, X., Zhang, H., Zhang, D., and Kim, S · 2017
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Bug localization with combination of deep learning and information retrieval
Lam, A. N., Nguyen, A. T., Nguyen, H. A., and Nguyen, T. N · 2017
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Automatic inference of code transforms for patch generation
Long, F., Amidon, P., and Rinard, M · 2017
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Automatically diagnosing and repairing error handling bugs in C
Tian, Y., and Ray, B · 2017
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Tortoise: Interactive system configuration repair
Weiss, A., Guha, A., and Brun, Y · 2017
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Better test cases for better automated program repair
Yang, J., Zhikhartsev, A., Liu, Y., and Tan, L · 2017
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The codrep machine learning on source code competition
Chen, Z., and Monperrus, M · 2018
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Deep code search
Gu, X., Zhang, H., and Kim, S · 2018
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Deep reinforcement learning for programming language correction
Gupta, R., Kanade, A., and Shevade, S. K · 2018
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Automatic software repair: A bibliography
Monperrus, M · 2018
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Machine learning-based prototyping of graphical user interfaces for mobile apps
Moran, K., Bernal-Cárdenas, C., Curcio, M., Bonett, R., and Poshyvanyk, D · 2018
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Dissection of a bug dataset: Anatomy of 395 patches from defects4j
Sobreira, V., Durieux, T., Delfim, F. M., Monperrus, M., and de Almeida Maia, M · 2018
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Using a probabilistic model to predict bug fixes
Soto, M., and Le Goues, C · 2018
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Deep learning similarities from different representations of source code
Tufano, M., Watson, C., Bavota, G., Di Penta, M., White, M., and Poshyvanyk, D · 2018
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An empirical investigation into learning bug-fixing patches in the wild via neural machine translation
Tufano, M., Watson, C., Bavota, G., Di Penta, M., White, M., and Poshyvanyk, D · 2018
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Online Appendix
Tufano, M., Watson, C., Bavota, G., Di Penta, M., White, M., and Poshyvanyk, D · 2018
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On learning meaningful code changes via neural machine translation
Tufano, M., Pantiuchina, J., Watson, C., Bavota, G., and Poshyvanyk, D · 2019
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Sorting and transforming program repair ingredients via deep learning code similarities
White, M., Tufano, M., Martinez, M., Monperrus, M., and Poshyvanyk, D · 2019
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