README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - software-engineering
  - conventional-commits
  - nlp
pretty_name: CCSBENCH
size_categories:
  - 10K<n<100K

CCSBENCH: A Fine-Grained Benchmark for Conventional Commits

1. Dataset Summary

CCSBENCH is a large-scale benchmark dataset specifically constructed for fine-grained Conventional Commits (CCS) classification research. Based on the CCS specification, it provides high-quality commit data with verified types and scopes across multiple programming languages.

The dataset contains 93,807 CCS-compliant commits extracted from 119 high-quality repositories. It spans a wide temporal range (April 2017 to February 2023) and includes 142 distinct commit types and 6,633 unique scopes.

2. Dataset Structure

Data Splits

We employ a strict temporal split to simulate real-world scenarios, ensuring that repositories are tracked continuously over time.

File	Count	Usage
train.json	74,581	Model training
valid.json	9,939	Hyperparameter tuning
test.json	9,287	Final performance evaluation

Language Distribution (by Repository)

The benchmark covers 9 key programming languages:

Language	Repositories	Language	Repositories
TypeScript	61	Rust	2
JavaScript	34	Java	2
Go	11	C++	1
Python	4	Kotlin	1
C#	3

3. Data Schema

Each record in the dataset is a JSON object representing a single commit. Below is a description of the key fields:

Field	Type	Description
hash	string	Unique identifier for the commit.
message	string	The full original commit message.
commit_type	string	The extracted CCS type (e.g., feat, fix, chore).
commit_scope	string	The extracted CCS scope (can be null if not provided).
is_CCS	integer	Binary indicator (1) for CCS compliance.
language	string	Programming language of the repository.
repo	string	Full name of the source repository (Owner/Repo).
mods	array	List of modified files, including diff, new_path, and old_path.
date	string	Timestamp of the commit.
license	string	Original license of the source repository.

4. Construction Principles

The construction of CCSBENCH prioritizes Scale, Quality, and Repository Continuity:

1. Keyword Detection: Identifying repositories explicitly adopting CCS ("conventionalcommits.org").
2. Format Validation: Using a robust parser to validate <type>[optional scope]: <description>.
3. Temporal Alignment: Discarding all commits preceding the repository’s formal adoption of CCS to ensure data purity.

5. Licensing

• Dataset License: Apache-2.0 (compatible with the licenses of the original repositories in CommitChronicle).
• Code License: MIT (for processing scripts).