CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model
Not on the map. Why?
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.
Builds on
16 of 28 references.
References · 16
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2018Deep contextualized word representationscited by 2,290
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
- 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
- 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 654
- 2017Automatically Generating Commit Messages from Diffs using Neural Machine Translationcited by 42
- 2019ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Rankingcited by 25
4 more
- 2017A Neural Architecture for Generating Natural Language Descriptions from Source Code Changescited by 23
- 2020CoreGen: Contextualized Code Representation Learning for Commit Message Generationcited by 15
- 2017Towards Automatic Generation of Short Summaries of Commitscited by 8
- 2019Generating Commit Messages from Git Diffscited by 5
Built on by
Citing papers per year: 2022: 3; 2023: 4; 2024: 5; 2025: 2; 2026: 2.
16 papers.
Sort:
By area:
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 189
- 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
- 2024Automatic Programming: Large Language Models and Beyondcited by 36
- 2022RACE: Retrieval-Augmented Commit Message Generationcited by 27
- 2023From Commit Message Generation to History-Aware Commit Message Completioncited by 16
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
- 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
- 2022CommitBART: A Large Pre-trained Model for GitHub Commitscited by 6
- 2025Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMscited by 6
- 2024COMET: Generating Commit Messages using Delta Graph Context Representationcited by 3
- 2024Automatic Pull Request Description Generation Using LLMs: A T5 Model Approachcited by 2
4 more
- 2024Edisum: Summarizing and Explaining Wikipedia Edits at Scalecited by 1
- 2022Can We Generate Shellcodes via Natural Language? An Empirical Studycited by 0
- 2026XSearch: Explainable Code Search via Concept-to-Code Alignmentcited by 0
- 2026Stylometric Defenses Against Author Impersonation in Software Repositoriescited by 0
- 2026Stylometric Defenses Against Author Impersonation in Software Repositories3 Aug 2026
- 2026XSearch: Explainable Code Search via Concept-to-Code Alignment15 May 2026
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence23 Nov 2025
- 2025Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs14 Jan 2025
- 2024Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach1 Aug 2024
- 2024Automatic Programming: Large Language Models and Beyond3 May 2024
- 2024Edisum: Summarizing and Explaining Wikipedia Edits at Scale4 Apr 2024
- 2024CommitBench: A Benchmark for Commit Message Generation8 Mar 2024
- 2024COMET: Generating Commit Messages using Delta Graph Context Representation2 Feb 2024
- 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code14 Nov 2023
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?10 Oct 2023
- 2023From Commit Message Generation to History-Aware Commit Message Completion15 Aug 2023
4 more
- 2023OctoPack: Instruction Tuning Code Large Language Models14 Aug 2023
- 2022CommitBART: A Large Pre-trained Model for GitHub Commits17 Aug 2022
- 2022RACE: Retrieval-Augmented Commit Message Generation5 Mar 2022
- 2022Can We Generate Shellcodes via Natural Language? An Empirical Study8 Feb 2022
- 2022RACE: Retrieval-Augmented Commit Message Generationcited by 27
- 2022RACE: Retrieval-Augmented Commit Message Generation5 Mar 2022
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 189
- 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
- 2024Automatic Programming: Large Language Models and Beyondcited by 36
- 2023From Commit Message Generation to History-Aware Commit Message Completioncited by 16
- 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
- 2022CommitBART: A Large Pre-trained Model for GitHub Commitscited by 6
- 2025Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMscited by 6
- 2024COMET: Generating Commit Messages using Delta Graph Context Representationcited by 3
- 2024Automatic Pull Request Description Generation Using LLMs: A T5 Model Approachcited by 2
- 2024Edisum: Summarizing and Explaining Wikipedia Edits at Scalecited by 1
- 2022Can We Generate Shellcodes via Natural Language? An Empirical Studycited by 0
12 most cited of 14 All 14 (JSON)
- 2026Stylometric Defenses Against Author Impersonation in Software Repositories3 Aug 2026
- 2026XSearch: Explainable Code Search via Concept-to-Code Alignment15 May 2026
- 2025Hierarchical Repository-Level Code Summarization for Business Applications Using Local LLMs14 Jan 2025
- 2024Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach1 Aug 2024
- 2024Automatic Programming: Large Language Models and Beyond3 May 2024
- 2024Edisum: Summarizing and Explaining Wikipedia Edits at Scale4 Apr 2024
- 2024CommitBench: A Benchmark for Commit Message Generation8 Mar 2024
- 2024COMET: Generating Commit Messages using Delta Graph Context Representation2 Feb 2024
- 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code14 Nov 2023
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?10 Oct 2023
- 2023From Commit Message Generation to History-Aware Commit Message Completion15 Aug 2023
- 2023OctoPack: Instruction Tuning Code Large Language Models14 Aug 2023
12 newest of 14 All 14 (JSON)