Contextualized Code Representation Learning for Commit Message Generation

@article{Nie2020ContextualizedCR,
  title={Contextualized Code Representation Learning for Commit Message Generation},
  author={Lun Yiu Nie and Cuiyun Gao and Zhicong Zhong and Wai Lam and Yang Liu and Zenglin Xu},
  journal={Neurocomputing},
  year={2020},
  volume={459},
  pages={97-107}
}
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13 Citations
Background Citations
8
Methods Citations
3

13 Citations

A large-scale empirical study of commit message generation: models, datasets and evaluation

This paper conducts a systematic and in-depth analysis of the state-of-the-art models and datasets for automatic commit message generation and collects a large-scale, information-rich, multi-programming-language, MCMD.

Dynamically Relative Position Encoding-Based Transformer for Automatic Code Edit

DTrans is designed with dynamically relative position encoding in the multi-head attention of Transformer, which can more accurately generate patches than the state-of-the-art methods and locate the lines to change with higher accuracy than the existing methods.

FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation

A novel commit message generation technique, FIRA, which first represents code changes via fine-grained graphs and then learns to generate commit messages automati-cally, which outperforms state-of-the-art techniques in terms of BLEU, ROUGE-L, and METEOR.

RACE: Retrieval-Augmented Commit Message Generation

RACE is proposed, a new retrieval-augmented neural commit message generation method, which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message.

What Makes a Good Commit Message?

A taxonomy based on recurring patterns in commit messages' expressions is developed, investigating whether “good” commit messages can be automatically identified and whether such automation could prompt developers to write better commit messages.

Code Structure Guided Transformer for Source Code Summarization

This paper proposes a novel approach named SG-Trans to incorporate code structural properties into Transformer, which injects the local symbolic information and global syntactic structure into the self-attention module of Transformer as inductive bias to capture the hierarchical characteristics of code.

A Survey on Machine Learning Techniques for Source Code Analysis

semantic graph, CFG Token-based, path-

Jointly Learning to Repair Code and Generate Commit Message

This work proposes a joint model that can both repair the program code and generate the commit message in a unified framework and enhances the cascaded method with different training approaches, including the teacher-student method, the multi-task method, and the back-translation method.

Disentangled Code Representation Learning for Multiple Programming Languages

The experimental results validate the superiority of the proposed disentangled code representation learning approach, compared to several baselines, across three types of downstream tasks, i.e., code clone detection, code translation, and code-to-code search.

CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model

The work is to develop a model that automatically writes the commit message, and releases 345K datasets consisting of code modification and commit messages in six programming languages.

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