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Real bug fixes found in open source repositories seem to be the perfect source for learning to localize and repair real bugs.
Sequencer: Sequence-to-sequence learning for end-to-end program repair
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Pre-trained Contextual Embedding of Source Code
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Incorporating copying mechanism in sequence-to-sequence learning
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Pointer sentinel mixture models
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Neural Machine Translation of Rare Words with Subword Units. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, Berlin, Germany, 1715–1725
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Deepbugs: A learning approach to name-based bug detection
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The adverse effects of code duplication in machine learning models of code. In Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software . 143–153
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Getafix: Learning to fix bugs automatically
Johannes Bader, Andrew Scott, Michael Pradel, and Satish Chandra. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
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Global relational models of source code. In International conference on learning representations
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Learning how to mutate source code from bug-fixes. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 301–312
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Association for Computational Linguistics, Online, 38–45
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Graph-based, self-supervised program repair from diagnostic feedback. In International Conference on Machine Learning . PMLR, 10799–10808
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An Exploratory Study of Debugging Episodes
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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. 2019b · 2019
Cited alongside, same era.
Neural program repair by jointly learning to localize and repair
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, and Rishabh Singh. 2019 · 2019
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Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al · 2020
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How Often Do Single-Statement Bugs Occur?: The ManySStuBs4J Dataset. In MSR . ACM, 573–577
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Big transfer (bit): General visual representation learning. In European conference on computer vision . Springer, 491–507
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Dlfix: Context-based code transformation learning for automated program repair. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering . 602–614
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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 . 101–114
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan. 2020 · 2020
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Self-supervised bug detection and repair
Miltiadis Allamanis, Henry Jackson-Flux, and Marc Brockschmidt. 2021 · 2021
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Pysstubs: Characterizing single-statement bugs in popular open-source python projects. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 520–524
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Semantic bug seeding: a learning-based approach for creating realistic bugs. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 906–918
Jibesh Patra and Michael Pradel. 2021 · 2021
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Break-it-fix-it: Unsupervised learning for program repair. In International Conference on Machine Learning . PMLR, 11941–11952
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On Distribution Shift in Learning-based Bug Detectors
Jingxuan He, Luca Beurer-Kellner, and Martin Vechev. 2022 · 2022
Closest in time.
Learning Realistic Mutations: Bug Creation for Neural Bug Detectors. In 2022 IEEE Conference on Software Testing, Verification and Validation (ICST) . IEEE, 162–173
Cedric Richter and Heike Wehrheim. 2022a · 2022
Closest in time.
TSSB-3M: Mining single statement bugs at massive scale
Cedric Richter and Heike Wehrheim. 2022b · 2022
Closest in time.