CoditT5: Pretraining for Source Code and Natural Language Editing

Author:

Zhang Jiyang1ORCID,Panthaplackel Sheena1,Nie Pengyu2,Li Junyi Jessy2,Gligoric Milos1

Affiliation:

1. The University of Texas at Austin, United States of America

2. The University of Texas at Austin, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference63 articles.

1. Wasi Ahmad , Saikat Chakraborty , Baishakhi Ray , and Kai-Wei Chang . 2021 . Unified Pre-training for Program Understanding and Generation. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2655–2668 . Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Unified Pre-training for Program Understanding and Generation. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2655–2668.

2. Dzmitry Bahdanau , Kyung Hyun Cho , and Yoshua Bengio . 2015 . Neural machine translation by jointly learning to align and translate . In International Conference on Learning Representations. Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations.

3. Satanjeev Banerjee and Alon Lavie . 2005 . METEOR: An automatic metric for MT evaluation with improved correlation with human judgments . In ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65–72 . Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 65–72.

4. Taylor Berg-Kirkpatrick , David Burkett , and Dan Klein . 2012 . An Empirical Investigation of Statistical Significance in NLP. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 995–1005 . Taylor Berg-Kirkpatrick, David Burkett, and Dan Klein. 2012. An Empirical Investigation of Statistical Significance in NLP. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 995–1005.

5. Avrim Blum and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Computational Learning Theory. 92–100. Avrim Blum and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Computational Learning Theory. 92–100.

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3. Grace: Language Models Meet Code Edits;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

4. LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning;2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE);2023-10-09

5. Enhancing Code Language Models for Program Repair by Curricular Fine-tuning Framework;2023 IEEE International Conference on Software Maintenance and Evolution (ICSME);2023-10-01

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