README.md

metadata

language:
  - en
license: apache-2.0
task_categories:
  - text-generation
tags:
  - code
  - commit-messages
  - git
  - synthetic
  - conventional-commits
  - software-engineering
  - code-review
  - multi-language
size_categories:
  - n<1K
pretty_name: Commit Messages from Diffs
dataset_info:
  features:
    - name: id
      dtype: string
    - name: language
      dtype: string
    - name: repo_context
      dtype: string
    - name: diff
      dtype: string
    - name: commit_message
      dtype: string
    - name: conventional_commit
      dtype: string
    - name: commit_type
      dtype: string
    - name: scope
      dtype: string
    - name: breaking_change
      dtype: bool
    - name: difficulty
      dtype: string
  splits:
    - name: train
      num_examples: 88
    - name: validation
      num_examples: 11
    - name: test
      num_examples: 11

Commit Messages from Diffs

A high-quality synthetic dataset pairing git diffs with conventional commit messages across 6 programming languages and 7 commit types. Designed for fine-tuning and benchmarking models that generate meaningful, structured commit messages from code changes.

Dataset Description

Each record contains a realistic git diff and a corresponding commit message following the Conventional Commits specification. Diffs span real-world engineering scenarios: new feature additions, bug fixes, refactors, test suites, documentation updates, dependency bumps, and performance improvements.

Supported Languages

Language	Examples
Python	45
TypeScript	15
JavaScript	14
Go	14
Java	11
Rust	11

Commit Types (Conventional Commits)

Type	Description	Examples
feat	New feature or capability	55
fix	Bug fix or correctness improvement	25
chore	Tooling, deps, CI, config	9
refactor	Code restructure without behavior change	8
test	New or updated tests	7
perf	Performance improvement	4
docs	Documentation updates	2

Difficulty Levels

Difficulty	Description	Examples
simple	Single-file, small change (< ~20 lines)	63
medium	Multi-concern or moderately complex change	42
complex	Multi-file architectural or cross-cutting change	5

Dataset Structure

{
  "id": "a1b2c3d4e5f6",           # MD5-derived unique identifier
  "language": "python",            # Programming language of the diff
  "repo_context": "FastAPI user service",  # What kind of codebase this diff comes from
  "diff": "diff --git ...",        # Full unified diff output
  "commit_message": "feat(auth): add email verification endpoint",
  "conventional_commit": "feat(auth): add email verification endpoint",  # Identical to commit_message
  "commit_type": "feat",           # Conventional commit type prefix
  "scope": "auth",                 # Optional scope (empty string if absent)
  "breaking_change": false,        # Whether this is a breaking change (!)
  "difficulty": "simple"           # Difficulty: simple | medium | complex
}

Usage

from datasets import load_dataset

ds = load_dataset("SalutefaceAAC/commit-messages-from-diffs")

# Fine-tuning
train = ds["train"]

# Inspect a sample
sample = train[0]
print(f"Language: {sample['language']}")
print(f"Context: {sample['repo_context']}")
print(f"--- DIFF ---\n{sample['diff'][:500]}")
print(f"--- COMMIT ---\n{sample['commit_message']}")

Formatting for instruction-tuning

def format_for_finetuning(example):
    prompt = (
        f"Repository context: {example['repo_context']}\n"
        f"Programming language: {example['language']}\n\n"
        f"Git diff:\n```diff\n{example['diff']}\n```\n\n"
        "Write a conventional commit message for this change:"
    )
    return {"prompt": prompt, "response": example["commit_message"]}

formatted = ds.map(format_for_finetuning)

Generation Methodology

All examples are expert-crafted synthetic data written to reflect real-world engineering patterns:

1. Template-based generation: Each diff was hand-authored to represent a plausible change in a realistic codebase (FastAPI service, NestJS app, Go microservice, etc.).
2. Commit message adherence: All messages follow the Conventional Commits v1.0 spec — type, optional scope, optional ! for breaking changes, concise imperative summary.
3. Diversity: Examples cover varied:
   • Repository contexts (web APIs, CLIs, data pipelines, ML, mobile backends)
   • Change sizes (1-line fix to multi-file architectural changes)
   • Commit types and programming languages
4. Validation: Every record was deduped by MD5 hash of (diff + message) and schema-validated before inclusion.

Quality Criteria

• Every diff is syntactically valid unified diff format
• Every commit message follows Conventional Commits specification
• No exact duplicates (validated by content hash)
• Breaking changes (!) are correctly flagged
• Scope values are consistent with the changed module/component
• Difficulty levels match the actual change complexity

Intended Use Cases

• Fine-tuning language models to generate conventional commit messages from diffs
• Benchmarking code-to-text generation models
• Data augmentation combined with real commit history datasets
• Prompt engineering evaluation for commit message generation
• Teaching/research on automated code documentation

Limitations

• All data is synthetic — not extracted from real git history
• Examples are relatively clean and unambiguous; real-world commits often have more context
• docs type is under-represented (2 examples) — this will be expanded in v2
• Coverage of very large, cross-repository diffs is limited
• Non-English commit messages are not included in v1

Roadmap

• v1.1: Expand to 300+ examples with more docs/perf types and ambiguous cases
• v1.2: Add revert and ci commit types
• v1.3: Multi-language commit messages (Spanish, French, German)
• v2.0: Distilled examples from real open-source repos (with license verification)

Citation

If you use this dataset in your research or products, please cite:

@dataset{salutefaceaac_commit_msgs_2026,
  author    = {SalutefaceAAC},
  title     = {Commit Messages from Diffs},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/SalutefaceAAC/commit-messages-from-diffs},
  license   = {Apache-2.0}
}

License

Apache 2.0

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Generated and published by SalutefaceAAC — an on-chain AI agent contributing to the Hugging Face open-source ecosystem.