mlsanityData to 14 Oct 2026

From Commit Message Generation to History-Aware Commit Message Completion

arXiv 2308.07655 ↗First posted 15 Aug 2023cs.SE cs.LGcited by 16

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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

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28 more
  1. 2019Gmail Smart Compose: Real-Time Assisted Writingcited by 98
  2. 2022Automating Code Review Activities by Large-Scale Pre-trainingcited by 98
  3. 2023ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Designcited by 66
  4. 2017Automatically Generating Commit Messages from Diffs using Neural Machine Translationcited by 42
  5. 2020CC2Vec: Distributed Representations of Code Changescited by 39
  6. 2020Fast and Memory-Efficient Neural Code Completioncited by 38
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 8; 2026: 4.

16 papers.

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  1. 2024IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generatorscited by 23
  2. 2025From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligencecited by 15
  3. 2024KADEL: Knowledge-Aware Denoising Learning for Commit Message Generationcited by 10
  4. 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
  5. 2024CommitBench: A Benchmark for Commit Message Generationcited by 8
  6. 2025LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLMcited by 8
  7. 2025ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Groundingcited by 4
  8. 2026Code Change Characteristics and Description Alignment: A Comparative Study of Agentic versus Human Pull Requestscited by 3
  9. 2026AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?cited by 2
  10. 2025CodeFuse-CommitEval: Towards Benchmarking LLM's Power on Commit Message and Code Change Inconsistency Detectioncited by 1
  11. 2024Towards Realistic Evaluation of Commit Message Generation by Matching Online and Offline Settingscited by 0
  12. 2025LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendationscited by 0
4 more
  1. 2025Challenge on Optimization of Context Collection for Code Completioncited by 0
  2. 2025Diff-XYZ: A Benchmark for Evaluating Diff Understandingcited by 0
  3. 2026Agentic Repository Mining: A Multi-Task Evaluationcited by 0
  4. 2026XSearch: Explainable Code Search via Concept-to-Code Alignmentcited by 0