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In recent years, Neural Machine Translator (NMT) has shown promise in automatically editing source code.
1907
Earlier work this paper cites.
R. Robbes and M. Lanza, “Example-based program transformation,” in International Conference on Model Driven Engineering Languages and Systems . Springer, 2008, pp. 174–188
2008
Earlier work this paper cites.
W. Tansey and E. Tilevich, “Annotation refactoring: inferring upgrade transformations for legacy applications,” in ACM Sigplan Notices , vol. 43, no. 10. ACM, 2008, pp. 295–312
2008
Earlier work this paper cites.
H. A. Nguyen, T. T. Nguyen, G. Wilson Jr, A. T. Nguyen, M. Kim, and T. N. Nguyen, “A graph-based approach to API usage adaptation,” in ACM Sigplan Notices , vol. 45, no. 10. ACM, 2010, pp. 302–321
2010
Earlier work this paper cites.
N. Meng, M. Kim, and K. S. McKinley, “Systematic editing: generating program transformations from an example,” ACM SIGPLAN Notices , vol. 46, no. 6, pp. 329–342, 2011
2011
Earlier work this paper cites.
2011
Earlier work this paper cites.
X. Ge, Q. L. DuBose, and E. Murphy-Hill, “Reconciling manual and automatic refactoring,” in Proceedings of the 34th International Conference on Software Engineering . IEEE Press, 2012, pp. 211–221
2012
Earlier work this paper cites.
——, “Lase: Locating and applying systematic edits by learning from examples,” In Proceedings of 35th International Conference on Software Engineering (ICSE) , pp. 502–511, 2013
2013
Earlier work this paper cites.
V. Raychev, M. Schäfer, M. Sridharan, and M. Vechev, “Refactoring with synthesis,” in ACM SIGPLAN Notices , vol. 48, no. 10. ACM, 2013, pp. 339–354
2013
Earlier work this paper cites.
H. A. Nguyen, A. T. Nguyen, T. T. Nguyen, T. N. Nguyen, and H. Rajan, “A study of repetitiveness of code changes in software evolution,” in Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering . IEEE Press, 2013, pp. 180–190
2013
Earlier work this paper cites.
J.-R. Falleri, F. Morandat, X. Blanc, M. Martinez, and M. Monperrus, “Fine-grained and accurate source code differencing,” in Proceedings of the 29th ACM/IEEE international conference on Automated software engineering . ACM, 2014, pp. 313–324
2014
Earlier work this paper cites.
R. Just, D. Jalali, and M. D. Ernst, “Defects4J: A database of existing faults to enable controlled testing studies for java programs,” in Proceedings of the 2014 International Symposium on Software Testing and Analysis . ACM, 2014, pp. 437–440
2014
Earlier work this paper cites.
B. Ray, M. Nagappan, C. Bird, N. Nagappan, and T. Zimmermann, “The uniqueness of changes: Characteristics and applications,” ser. MSR ’15. ACM, 2015
2015
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
N. Meng, L. Hua, M. Kim, and K. S. McKinley, “Does automated refactoring obviate systematic editing?” in Proceedings of the 37th International Conference on Software Engineering-Volume 1 . IEEE Press, 2015, pp. 392–402
2015
Earlier work this paper cites.
B. Ray, V. Hellendoorn, S. Godhane, Z. Tu, A. Bacchelli, and P. Devanbu, “On the” naturalness” of buggy code,” in 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE) . IEEE, 2016, pp. 428–439
2016
Earlier work this paper cites.
R. Rolim, G. Soares, L. D’Antoni, O. Polozov, S. Gulwani, R. Gheyi, R. Suzuki, and B. Hartmann, “Learning syntactic program transformations from examples,” in Proceedings of the 39th International Conference on Software Engineering . IEEE Press, 2017, pp. 404–415
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30 , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
P. Yin and G. Neubig, “A syntactic neural model for general-purpose code generation,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , vol. 1, 2017, pp. 440–450
2017
Earlier work this paper cites.
Y. Zhou and A. Sharma, “Automated identification of security issues from commit messages and bug reports,” in Proceedings of the 2017 11th joint meeting on foundations of software engineering , 2017, pp. 914–919
2017
Cited alongside, same era.
M. Tufano, C. Watson, G. Bavota, M. Di Penta, M. White, and D. Poshyvanyk, “An empirical investigation into learning bug-fixing patches in the wild via neural machine translation,” 2018
2018
Cited alongside, same era.
Z. Liu, X. Xia, A. E. Hassan, D. Lo, Z. Xing, and X. Wang, “Neural-machine-translation-based commit message generation: How far are we?” in 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2018, pp. 373–384
2018
Cited alongside, same era.
T. Kudo and J. Richardson, “SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations , Nov. 2018, pp. 66–71
S. Chakraborty, Y. Ding, M. Allamanis, and B. Ray, “Codit: Code editing with tree-based neural models,” IEEE Transactions on Software Engineering , vol. 1, pp. 1–1, 2020
2020
Later among the works it cites.
T. Lutellier, H. V. Pham, L. Pang, Y. Li, M. Wei, and L. Tan, “Coconut: combining context-aware neural translation models using ensemble for program repair,” in Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2020, pp. 101–114
2020
Later among the works it cites.
W. Wang, G. Li, S. Shen, X. Xia, and Z. Jin, “Modular tree network for source code representation learning,” ACM Transactions on Software Engineering and Methodology (TOSEM) , vol. 29, no. 4, pp. 1–23, 2020
2020
Later among the works it cites.
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang, and M. Zhou, “CodeBERT: A pre-trained model for programming and natural languages,” in Findings of the Association for Computational Linguistics: EMNLP 2020 , Nov. 2020, pp. 1536–1547
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2018
Cited alongside, same era.
M. R. Parvez, S. Chakraborty, B. Ray, and K.-W. Chang, “Building language models for text with named entities,” 2018
2018
Cited alongside, same era.
K. Gallaba, C. Macho, M. Pinzger, and S. McIntosh, “Noise and heterogeneity in historical build data: an empirical study of travis ci,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , 2018, pp. 87–97
2018
Cited alongside, same era.
2019
Cited alongside, same era.
M. Tufano, C. Watson, G. Bavota, M. D. Penta, M. White, and D. Poshyvanyk, “An empirical study on learning bug-fixing patches in the wild via neural machine translation,” ACM Transactions on Software Engineering and Methodology (TOSEM) , vol. 28, no. 4, pp. 1–29, 2019
2019
Cited alongside, same era.
Z. Chen, S. J. Kommrusch, M. Tufano, L.-N. Pouchet, D. Poshyvanyk, and M. Monperrus, “Sequencer: Sequence-to-sequence learning for end-to-end program repair,” IEEE Transactions on Software Engineering , 2019
2019
Cited alongside, same era.
B. Wei, G. Li, X. Xia, Z. Fu, and Z. Jin, “Code generation as a dual task of code summarization,” in Advances in Neural Information Processing Systems 32 , 2019, pp. 6563–6573
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
W. U. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “A transformer-based approach for source code summarization,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL) , 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Svyatkovskiy, S. K. Deng, S. Fu, and N. Sundaresan, “Intellicode compose: Code generation using transformer,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 1433–1443
2020
Later among the works it cites.
R.-M. Karampatsis, H. Babii, R. Robbes, C. Sutton, and A. Janes, “Big code!= big vocabulary: Open-vocabulary models for source code,” in 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) . IEEE, 2020, pp. 1073–1085
2020
Later among the works it cites.
Y. Ding, B. Ray, P. Devanbu, and V. J. Hellendoorn, “Patching as translation: the data and the metaphor,” in 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2020, pp. 275–286
2020
Later among the works it cites.
D. Tarlow, S. Moitra, A. Rice, Z. Chen, P.-A. Manzagol, C. Sutton, and E. Aftandilian, “Learning to fix build errors with graph2diff neural networks,” in Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops , 2020, pp. 19–20
2020
Later among the works it cites.
W. Wang, G. Li, B. Ma, X. Xia, and Z. Jin, “Detecting code clones with graph neural network and flow-augmented abstract syntax tree,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2020, pp. 261–271
2020
Later among the works it cites.
H. Xu, S. Fan, Y. Wang, Z. Huang, H. Xu, and P. Xie, “Tree2tree structural language modeling for compiler fuzzing,” in International Conference on Algorithms and Architectures for Parallel Processing . Springer, 2020, pp. 563–578
2020
Later among the works it cites.
2021
Closest in time.
D. Guo, S. Ren, S. Lu, Z. Feng, D. Tang, S. Liu, L. Zhou, N. Duan, J. Yin, D. Jiang et al. , “Graphcodebert: Pre-training code representations with data flow,” in International Conference on Learning Representations , 2021
2021
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W. U. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “Unified pre-training for program understanding and generation,” in 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) , 2021
2021
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2021
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2021
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2021
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S. Chakraborty, R. Krishna, Y. Ding, and B. Ray, “Deep learning based vulnerability detection: Are we there yet,” IEEE Transactions on Software Engineering , pp. 1–1, 2021
2021
Closest in time.