Combining Code Context and Fine-grained Code Difference for Commit Message Generation

Author:

Xu Shengbin1,Yao Yuan1,Xu Feng1,Gu Tianxiao2,Tong Hanghang3

Affiliation:

1. Nanjing University, China

2. Tiktok Inc., United States

3. University of Illinois Urbana-Champaign, United States

Publisher

ACM

Reference41 articles.

1. A Transformer-based Approach for Source Code Summarization

2. Miltiadis Allamanis , Hao Peng , and Charles Sutton . 2016 . A convolutional attention network for extreme summarization of source code . In Proceedings of International conference on machine learning (ICML). Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016. A convolutional attention network for extreme summarization of source code. In Proceedings of International conference on machine learning (ICML).

3. Uri Alon , Meital Zilberstein , Omer Levy , and Eran Yahav . 2019 . code2vec: Learning distributed representations of code . In Proceedings of the ACM on Programming Languages (POPL). Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. code2vec: Learning distributed representations of code. In Proceedings of the ACM on Programming Languages (POPL).

4. Pavol Bielik , Veselin Raychev , and Martin Vechev . 2016 . PHOG: probabilistic model for code . In Proceedings of International conference on machine learning (ICML). Pavol Bielik, Veselin Raychev, and Martin Vechev. 2016. PHOG: probabilistic model for code. In Proceedings of International conference on machine learning (ICML).

5. Automatically documenting program changes

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1. Multi-grained contextual code representation learning for commit message generation;Information and Software Technology;2024-03

2. Mucha: Multi-channel based Code Change Representation Learning for Commit Message Generation;2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS);2023-10-22

3. Summarize Me: The Future of Issue Thread Interpretation;2023 IEEE International Conference on Software Maintenance and Evolution (ICSME);2023-10-01

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