---
license: mit
task_categories:
- text-classification
- text-generation
- sentence-similarity
- text2text-generation
language:
- en
tags:
- code
size_categories:
- 100K<n<1M
---
# GitHub Repo Metadata 5★ — Developer History and Profiling Dataset

📘 Paper (FSE 2025)
[💻 Codebase](https://github.com/SODAForge/SODAOpt)  
[📊 Source Dataset on Kaggle](https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars)

## Dataset Summary

This dataset provides a processed and enriched version of the "GitHub Repository Metadata with 5 Stars" dataset, reformatted to support developer modeling and task recommendation research.

We provide several views of the data that are tailored for:
- Developer-level sequence modeling
- Socio-technical profiling
- Text-based representation learning
- Hybrid retrieval and recommendation tasks

This dataset is used in our [FSE 2025 paper](https://doi.org/10.1145/3696630.3731437):  
**SODAOpt: Socio-Demographic and Textual Adaptive Fusion for Optimizing Developer Task Assignment**.

## Dataset Structure

This dataset is available in `.parquet` format and includes:

| File | Description |
|------|-------------|
| `textual_history.parquet` | Concatenated per-user textual views of repositories |
| `id_history.parquet` | Developer → repository interaction histories using hashed IDs |
| `user_descriptions.parquet` | Structured per-user summaries with stars, forks, top languages |
| `repo_info.parquet` | Clean table of repositories with hashed `repo_id` and `language_id` |
| `mappings/item_id_map.json` | Mapping of hashed `repo_id` to sequential numeric `item_id` |
| `mappings/language_mapping.parquet` | Mapping of language names to numeric `language_id` |

## Use Cases

- 🔍 Retrieval-based developer-task matching  
- 🧠 Developer embedding learning  
- 🧮 Evaluation of sequence models in software engineering  
- 🧬 Pretraining/finetuning for software-oriented LLMs

## How to Cite

```
@misc{zjkarina_2025_sodaopt,
    title = {SODAOpt: Social Dialogue Optimization Dataset},
    author = {Karina Romanova and Sergey Senichev and Lina Veltman and Ivan Nasonov and Andrey Kuznetsov and Ilya Makarov},
    month = {April},
    year = {2025},
    url = {https://huggingface.co/datasets/zjkarina/SODAOpt}
}
```

### Original Dataset
> Pelmers. (2023). *GitHub Repository Metadata with 5+ Stars*. Kaggle.  
> https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars

### Derived Work
> Romanova, K., Senichev, S., Veltman, L., Nasonov, I., Kuznetsov, A., & Makarov, I. (2025).  
> *SODAOpt: Socio-Demographic and Textual Adaptive Fusion for Optimizing Developer Task Assignment*.  
> In Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering (FSE ’25).