The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
CoCoNuT: combining context-aware neural translation models using ensemble for program repair
Abstract
Automated generate-and-validate (GV) program repair techniques (APR) typically rely on hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a significant amount of manual effort to discover and it is hard to adapt these rules to different programming languages.
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Cites
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Self-citationConvolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 18×
“Figure 6 shows the same bug in both the Java (Figure 6a) and Python (Figure 6b) We compare CoCoNuT with DLFix [42] and SequenceR [13], two state-of-the-art NMT-based APR techniques and three other state-of-the-art NMT architectures (i.e., LSTM [56], Transformer [78], and FConv [20]).”
BackgroundMethodInfluentialDefects4J: a database of existing faults to enable controlled testing studies for Java programs
Just, R., Jalali, D., & Ernst, M. D. (2014) · Proceedings of the 2014 International Symposium on Software Testing and Analysis · doi:10.1145/2610384.2628055 · Cited by 1,447
An analysis of patch plausibility and correctness for generate-and-validate patch generation systems
Qi, Z., Long, F., Achour, S., & Rinard, M. (2015) · Proceedings of the 2015 International Symposium on Software Testing and Analysis · doi:10.1145/2771783.2771791 · Cited by 420
Shaping program repair space with existing patches and similar code
Jiang, J., Xiong, Y., Zhang, H., Gao, Q., & Chen, X. (2018) · Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis · doi:10.1145/3213846.3213871 · Cited by 329
The ManyBugs and IntroClass benchmarks for automated repair of C programs
Le Goues, C., Holtschulte, N., Smith, E. K., Brun, Y., Devanbu, P., Forrest, S., & Weimer, W. (2015) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2015.2454513 · Cited by 326 · cited 5×
“On the ManyBugs benchmark, CoCoNuT fixes seven bugs, outperforming GenProg, Kali, and RSRepair but is outperformed by other techniques.”
MethodInfluentialAre deep neural networks the best choice for modeling source code?
Hellendoorn, V. J., & Devanbu, P. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106290 · Cited by 292
A Survey of Machine Learning for Big Code and Naturalness
Allamanis, M., Barr, E. T., Devanbu, P., & Sutton, C. (2019) · ACM Computing Surveys · doi:10.1145/3212695 · Cited by 150
Cited by
Unified Pre-training for Program Understanding and Generation
Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.211 · Cited by 642
Automated Program Repair in the Era of Large Pre-trained Language Models
Xia, C. S., Wei, Y., & Zhang, L. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00129 · Cited by 433
An Analysis of the Automatic Bug Fixing Performance of ChatGPT
Sobania, D., Briesch, M., Hanna, C., & Petke, J. (2023) · 2023 IEEE/ACM International Workshop on Automated Program Repair (APR) · doi:10.1109/apr59189.2023.00012 · Cited by 338
Automated Repair of Programs from Large Language Models
Fan, Z., Gao, X., Mirchev, M., Roychoudhury, A., & Tan, S. H. (2023) · 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) · doi:10.1109/icse48619.2023.00128 · Cited by 256
Less training, more repairing please: revisiting automated program repair via zero-shot learning
Xia, C. S., & Zhang, L. (2022) · Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3540250.3549101 · Cited by 231
Metadata
- Authors
- Lutellier, T., Pham, H. V., Pang, L., Li, Y., Wei, M., & Tan, L.
- Venue
- Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2020-07-18
- Pages
- 101-114
- OpenAlex
- W3043761819
- Cited by
- 344
- References
- 8
- FWCI
- 29.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lutellier, T., Pham, H. V., Pang, L., Li, Y., Wei, M., & Tan, L. (2020). CoCoNuT: combining context-aware neural translation models using ensemble for program repair. Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. https://doi.org/10.1145/3395363.3397369
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