From Commit Message Generation to History-Aware Commit Message Completion
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Abstract (arXiv)
Commit messages are crucial to software development, allowing developers to track changes and collaborate effectively. Despite their utility, most commit messages lack important information since writing high-quality commit messages is tedious and time-consuming. The active research on commit message generation (CMG) has not yet led to wide adoption in practice. We argue that if we could shift the focus from commit message generation to commit message completion and use previous commit history as additional context, we could significantly improve the quality and the personal nature of the resulting commit messages. In this paper, we propose and evaluate both of these novel ideas. Since the existing datasets lack historical data, we collect and share a novel dataset called CommitChronicle, containing 10.7M commits across 20 programming languages. We use this dataset to evaluate the completion setting and the usefulness of the historical context for state-of-the-art CMG models and GPT-3.5-turbo. Our results show that in some contexts, commit message completion shows better results than generation, and that while in general GPT-3.5-turbo performs worse, it shows potential for long and detailed messages. As for the history, the results show that historical information improves the performance of CMG models in the generation task, and the performance of GPT-3.5-turbo in both generation and completion.
Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 40 of its 71 references that are in the map. First posted on arXiv 15 Aug 2023.
Builds on
40 of 71 references.
References · 40
- 2017Attention Is All You Needcited by 60,361
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2023GPT-4 Technical Reportcited by 18,396
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
- 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
- 2020IntelliCode Compose: Code Generation Using Transformercited by 227
- 2022Practical Program Repair in the Era of Large Pre-trained Language Modelscited by 133
- 2022Few-shot training LLMs for project-specific code-summarizationcited by 129
- 2022Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproductioncited by 111
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- 2019Gmail Smart Compose: Real-Time Assisted Writingcited by 98
- 2022Automating Code Review Activities by Large-Scale Pre-trainingcited by 98
- 2023ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Designcited by 66
- 2017Automatically Generating Commit Messages from Diffs using Neural Machine Translationcited by 42
- 2020CC2Vec: Distributed Representations of Code Changescited by 39
- 2020Fast and Memory-Efficient Neural Code Completioncited by 38
- 2023Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Modelscited by 38
- 2022Automatic Code Documentation Generation Using GPT-3cited by 37
- 2022RACE: Retrieval-Augmented Commit Message Generationcited by 27
- 2019ATOM: Commit Message Generation Based on Abstract Syntax Tree and Hybrid Rankingcited by 25
- 2017A Neural Architecture for Generating Natural Language Descriptions from Source Code Changescited by 23
- 2021Sampling Projects in GitHub for MSR Studiescited by 21
- 2021On the Evaluation of Commit Message Generation Models: An Experimental Studycited by 20
- 2022What Makes a Good Commit Message?cited by 20
- 2021CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Modelcited by 16
- 2020CoreGen: Contextualized Code Representation Learning for Commit Message Generationcited by 15
- 2019Automatic Generation of Pull Request Descriptionscited by 13
- 2020Detecting and Characterizing Bots that Commit Codecited by 10
- 2020A ground-truth dataset and classification model for detecting bots in GitHub issue and PR commentscited by 10
- 2022All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logscited by 9
- 2017Towards Automatic Generation of Short Summaries of Commitscited by 8
- 2022A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scriptscited by 7
- 2021An Empirical Study on Code Comment Completioncited by 5
- 2020On the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generationcited by 4
- 2021PyNose: A Test Smell Detector For Pythoncited by 4
- 2021Evaluating a bot detection model on git commit messagescited by 2
- 2021Jointly Learning to Repair Code and Generate Commit Messagecited by 2
- 2021gambit -- An Open Source Name Disambiguation Tool for Version Control Systemscited by 1
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Citing papers per year: 2024: 4; 2025: 8; 2026: 4.
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- 2024IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generatorscited by 23
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generationcited by 10
- 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
- 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
- 2025LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLMcited by 8
- 2025ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Groundingcited by 4
- 2026Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requestscited by 3
- 2026AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?cited by 2
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detectioncited by 1
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settingscited by 0
- 2025LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendationscited by 0
4 more
- 2026AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?3 Jul 2026
- 2026XSearch: Explainable Code Search via Concept-to-Code Alignment15 May 2026
- 2026Agentic Repository Mining: A Multi-Task Evaluation6 May 2026
- 2026Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests24 Jan 2026
- 2025LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM1 Dec 2025
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection25 Nov 2025
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence23 Nov 2025
- 2025Diff-XYZ: A Benchmark for Evaluating Diff Understanding14 Oct 2025
- 2025Challenge on Optimization of Context Collection for Code Completion5 Oct 2025
- 2025LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations13 Aug 2025
- 2025Towards an Understanding of Context Utilization in Code Intelligence11 Apr 2025
- 2025ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding27 Mar 2025
4 more
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings15 Oct 2024
- 2024CommitBench: A Benchmark for Commit Message Generation8 Mar 2024
- 2024IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators6 Mar 2024
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation16 Jan 2024
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
- 2026Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requestscited by 3
- 2026AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?cited by 2
- 2026Agentic Repository Mining: A Multi-Task Evaluationcited by 0
- 2026AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?3 Jul 2026
- 2026Agentic Repository Mining: A Multi-Task Evaluation6 May 2026
- 2026Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requests24 Jan 2026
- 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence23 Nov 2025
- 2024IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generatorscited by 23
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generationcited by 10
- 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
- 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
- 2025ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Groundingcited by 4
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detectioncited by 1
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settingscited by 0
- 2025Challenge on Optimization of Context Collection for Code Completioncited by 0
- 2025Diff-XYZ: A Benchmark for Evaluating Diff Understandingcited by 0
- 2026XSearch: Explainable Code Search via Concept-to-Code Alignmentcited by 0
- 2026XSearch: Explainable Code Search via Concept-to-Code Alignment15 May 2026
- 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detection25 Nov 2025
- 2025Diff-XYZ: A Benchmark for Evaluating Diff Understanding14 Oct 2025
- 2025Challenge on Optimization of Context Collection for Code Completion5 Oct 2025
- 2025Towards an Understanding of Context Utilization in Code Intelligence11 Apr 2025
- 2025ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding27 Mar 2025
- 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settings15 Oct 2024
- 2024CommitBench: A Benchmark for Commit Message Generation8 Mar 2024
- 2024IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators6 Mar 2024
- 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation16 Jan 2024