mlsanityData to 14 Oct 2026

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

arXiv 2105.14242 ↗First posted 29 May 2021cs.CLcited by 16

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Abstract (arXiv)

Commit message is a document that summarizes source code changes in natural language. A good commit message clearly shows the source code changes, so this enhances collaboration between developers. Therefore, our work is to develop a model that automatically writes the commit message. To this end, we release 345K datasets consisting of code modification and commit messages in six programming languages (Python, PHP, Go, Java, JavaScript, and Ruby). Similar to the neural machine translation (NMT) model, using our dataset, we feed the code modification to the encoder input and the commit message to the decoder input and measure the result of the generated commit message with BLEU-4. Also, we propose the following two training methods to improve the result of generating the commit message: (1) A method of preprocessing the input to feed the code modification to the encoder input. (2) A method that uses an initial weight suitable for the code domain to reduce the gap in contextual representation between programming language (PL) and natural language (NL). Training code, dataset, and pre-trained weights are available at https://github.com/graykode/commit-autosuggestions

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 16 of its 28 references that are in the map. First posted on arXiv 29 May 2021.

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16 of 28 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  5. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  6. 2018Deep contextualized word representationscited by 2,290
  7. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  8. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
  9. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  10. 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 654
  11. 2017Automatically Generating Commit Messages from Diffs using Neural Machine Translationcited by 42
  12. 2019ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Rankingcited by 25
4 more
  1. 2017A Neural Architecture for Generating Natural Language Descriptions from Source Code Changescited by 23
  2. 2020CoreGen: Contextualized Code Representation Learning for Commit Message Generationcited by 15
  3. 2017Towards Automatic Generation of Short Summaries of Commitscited by 8
  4. 2019Generating Commit Messages from Git Diffscited by 5

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Citing papers per year: 2022: 3; 2023: 4; 2024: 5; 2025: 2; 2026: 2.

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