Lin18/github_issues_sample

Dataset Card for Github Issues - TensorFlow

Dataset Details

Dataset Description

This dataset contains 50 open issues collected from the public TensorFlow GitHub repository. Each record includes the issue ID, title, body text, creation date, anonymized user ID, body length, and a flag indicating whether the issue mentions a bug. The dataset has been structured for analysis and learning purposes.

• Curated by: Lin Shi
• Language(s) (NLP): English
• License: Create Commons Zero v1.0 Universal (CC0 1.0)

Dataset Sources [optional]

• Repository: https://github.com/tensorflow/tensorflow

Uses

Direct Use

This dataset can be used for text analysis, summarization, or bug detection exercises.

Out-of-Scope Use

Not intended for production software bug tracking or any commercial purpose. User information has been anonymized.

Dataset Structure

• id: int64, unique identifier for each issue

• title: string, issue title

• body: string, issue content

• created_at: string, creation date

• user: string, anonymized user ID

• body_length: int64, number of characters in the body

• has_bug: int64, 1 if the body mentions 'bug', otherwise 0

• Split: train, 50 examples

Dataset Creation

Curation Rationale

This dataset was created to provide a small, structured sample of GitHub issues for learning and experimentation in text analysis and bug detection.

Source Data

Collected via the GitHub API using requests library. Data was filtered and structured in a Pandas DataFrame. Usernames were anonymized for privacy.

Data Collection and Processing

The latest 50 open issues were retrieved from the TensorFlow GitHub repository. Each issue's ID, title, body, creation date, and username were extracted. Usernames were anonymized using a hashing method to protect privacy. Additional derived fields include body_length and has_bug.

Who are the source data producers?

The source data producers are the contributors of the TensorFlow repository on GitHub. No personal information beyond publicly available usernames (which were anonymized) is included.

Annotation process

No manual annotation was performed for this dataset.
The only derived labels are programmatically generated fields such as body_length and
has_bug, which were computed automatically using simple text-processing rules.
No annotation tools or human annotators were involved.

Who are the annotators?

There were no human annotators.
All derived fields were generated automatically through Python code written by the dataset curator (Lin Shi).

Personal and Sensitive Information

All usernames have been anonymized, and no sensitive or private information is included.
The dataset only contains publicly available GitHub issue text.
It is intended solely for educational use as part of a TAFE coursework assignment.

Bias, Risks, and Limitations

The dataset only contains 50 open issues from one repository, so it is not representative of all GitHub projects or issue types. Derived fields like has_bug are simplistic and may not fully capture actual bugs.

Recommendations

Users should be aware that this dataset is for educational purposes only and should not be used for production bug tracking or commercial analysis.

BibTeX:

No formal citation is available. Please cite the TensorFlow GitHub repository if needed.

APA:

No formal citation available. Refer to the TensorFlow GitHub repository for source data.

Dataset Card Contact

For questions about this dataset, please contact:

• Name: Lin Shi
• Purpose: Educational use only (TAFE coursework)