metadata

license: other
language:
  - code
  - en
task_categories:
  - text-classification
tags:
  - code
  - commit_message_generation
configs:
  - config_name: default
    data_files:
      - split: test
        path: data.jsonl

Commit Message Quality dataset

This is the dataset for commit message quality classification, used during processing of Commit Message Generation dataset from 🏟️ Long Code Arena benchmark.

This is a cleaned and relabeled version of the dataset from 📜 "Commit Message Matters: Investigating Impact and Evolution of Commit Message Quality", ICSE'23. We drop "Neither Why nor What" examples, clean all the external references (URLs, issues/PR references) from messages and manually label each sample with the goal of training a binary commit message quality classifier for data filtering in mind.

How-to

Load the data via load_dataset:

```
from datasets import load_dataset

dataset = load_dataset("saridormi/commit-message-quality", split="test")
```

Note that all the data we have is considered to be in the test split.

Dataset Structure

Each example has the following fields:

Field	Description
`url`	Link to commit on GitHub.
`original_message`	Commit message as it was in the original dataset.
`message`	Commit message cleaned from external references.
`original_label`	Commit message label as it was in the original dataset (`Why and What`/`No Why`/`No What`).
`is_good`	Whether the commit message serves as a good example of a high quality commit message (boolean).
`is_bad`	Whether the commit message serves as a good example of a low quality commit message (boolean).
`binary_label`	Commit message label: `1` for high quality messages, `0` for low quality messages, `null` for messages not recommended to consider for classifier training.

Data point example:

{"url":"https://github.com/spring-projects/spring-boot/commit/7080500db9ecf1cf78ad23503280c713bb6e8649",
 "original_message":"Upgrade to Commons Lang3 3.6 \n  \n Closes gh-9661",
 "message":"Upgrade to Commons Lang3 3.6",
 "original_label":"Why and What",
 "is_good": False,
 "is_bad": True,
 "binary_label":0.0,
}