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The joint task of bug localization and program repair is an integral part of the software development process.
Codesearchnet challenge: Evaluating the state of semantic code search
Husain, H., Wu, H.-H., Gazit, T., Allamanis, M., and Brockschmidt, M. (2019) · 1909
Earlier work this paper cites.
Defects4j: A database of existing faults to enable controlled testing studies for java programs
Just, R., Jalali, D., and Ernst, M. D. (2014) · 2014
Earlier work this paper cites.
The dstar method for effective software fault localization
Wong, W. E., Debroy, V., Gao, R., and Li, Y. (2014) · 2014
Earlier work this paper cites.
Fixing recurring crash bugs via analyzing q|&a sites
Gao, Q., Zhang, H., Wang, J., Xiong, Y., Zhang, L., and Mei, H. (2015) · 2015
Earlier work this paper cites.
An analysis of patch plausibility and correctness for generate-and-validate patch generation systems
Qi, Z., Long, F., Achour, S., and Rinard, M. (2015) · 2015
Earlier work this paper cites.
Learning to represent programs with graphs
Allamanis, M., Brockschmidt, M., and Khademi, M. (2017) · 2017
Earlier work this paper cites.
Semantic code repair using neuro-symbolic transformation networks
Devlin, J., Uesato, J., Singh, R., and Kohli, P. (2017) · 2017
Earlier work this paper cites.
Quixbugs: A multi-lingual program repair benchmark set based on the quixey challenge
Lin, D., Koppel, J., Chen, A., and Solar-Lezama, A. (2017) · 2017
Earlier work this paper cites.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Chen, Z., Kommrusch, S., Tufano, M., Pouchet, L.-N., Poshyvanyk, D., and Monperrus, M. (2018) · 2018
Cited alongside, same era.
Understanding back-translation at scale
Edunov, S., Ott, M., Auli, M., and Grangier, D. (2018) · 2018
Cited alongside, same era.
Empirical review of java program repair tools: A large-scale experiment on 2,141 bugs and 23,551 repair attempts
Durieux, T., Madeiral, F., Martinez, M., and Abreu, R. (2019) · 2019
Cited alongside, same era.
Pre-trained contextual embedding of source code
Kanade, A., Maniatis, P., Balakrishnan, G., and Shi, K. (2019) · 2019
Cited alongside, same era.
An empirical study of incorporating pseudo data into grammatical error correction
Kiyono, S., Suzuki, J., Mita, M., Mizumoto, T., and Inui, K. (2019) · 2019
Cited alongside, same era.
Deepdelta: Learning to repair compilation errors
A comprehensive study of automatic program repair on the quixbugs benchmark
Ye, H., Martinez, M., Durieux, T., and Monperrus, M. (2019) · 2019
Later among the works it cites.
PyMT5: multi-mode translation of natural language and python code with transformers
Clement, C., Drain, D., Timcheck, J., Svyatkovskiy, A., and Sundaresan, N. (2020) · 2020
Later among the works it cites.
Reformer: The efficient transformer
Kitaev, N., Kaiser, L., and Levskaya, A. (2020) · 2020
Later among the works it cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2020) · 2020
Later among the works it cites.
Coconut: Combining context-aware neural translation models using ensemble for program repair
Lutellier, T., Pham, H. V., Pang, L., Li, Y., Wei, M., and Tan, L. (2020) · 2020
Later among the works it cites.
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Mesbah, A., Rice, A., Johnston, E., Glorioso, N., and Aftandilian, E. (2019) · 2019
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2019) · 2019
Cited alongside, same era.
An empirical study on learning bug-fixing patches in the wild via neural machine translation
Tufano, M., Watson, C., Bavota, G., Penta, M. D., White, M., and Poshyvanyk, D. (2019) · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. (2020)
Cited in the paper.
Intellicode compose: Code generation using transformer
Svyatkovskiy, A., Deng, S. K., Fu, S., and Sundaresan, N. (2020) · 2020
Later among the works it cites.
Generating bug-fixes using pretrained transformers
Drain, D., Wu, C., Svyatkovskiy, A., and Sundaresan, N. (2021) · 2021
Closest in time.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C. B., Drain, D., Jiang, D., Tang, D., Li, G., Zhou, L., Shou, L., Zhou, L., Tufano, M., Gong, M., Zhou, M., Duan, N., Sundaresan, N., Deng, S. K., Fu, S., and Liu, S. (2021) · 2021
Closest in time.