---
dataset_info:
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    num_examples: 360
  - name: train
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    num_examples: 288
  - name: test
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    num_examples: 72
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  dataset_size: 2558491
configs:
- config_name: default
  data_files:
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  - split: train
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  - split: test
    path: data/test-*
---

# Software Readability Dataset

This repository contains the dataset used to build and evaluate the readability model presented in:

> **A General Software Readability Model**
> *Jonathan Dorn & Westley Weimer, University of Virginia*

The dataset consists of human-annotated code snippets sampled from real open-source projects and labeled for perceived readability. It is the largest such dataset collected for software readability research to date.

---

## 📦 Dataset Summary

| Property              | Value                                                              |
| --------------------- | ------------------------------------------------------------------ |
| Total snippets        | **360**                                                            |
| Programming languages | **Java, Python, CUDA**                                             |
| Snippet lengths       | ~10, ~30, ~50 lines                                                |
| Human annotations     | **≈ 5,468 annotators**                                             |
| Total ratings         | **≈ 76,741 readability votes**                                     |
| Rating scale          | 1–5 Likert (1 = very unreadable, 5 = very readable)                |
| Annotator background  | students + industry (1,000+ with 5+ years professional experience) |
| Source projects       | 30 open-source repositories                                        |

The dataset was collected through an IRB-approved online survey. Each participant viewed 20 random snippets and rated their readability. Samples were drawn automatically and uniformly from repositories, without manual curation, to avoid bias.

---

## 🧠 Tasks Supported

This dataset supports several research directions:

### 🎯 Core Tasks

* readability prediction (regression or classification)
* feature engineering for code comprehension
* cross-language readability comparison


## 🌍 Languages & Projects

Languages sampled:

* **Java** (large, object-oriented)
* **Python** (indentation-sensitive)
* **CUDA** (GPU programming)

Each sourced from 10 real open-source repos (30 total), including widely-used projects like:

* Liferay Portal
* SQuirreL SQL Client
* Docutils
* GPUMLib

(see paper for full list)

---

## 📝 Annotation Protocol

* ratings used a 1–5 Likert scale:

  * 1 → very unreadable
  * 5 → very readable
* code was syntax-highlighted in survey
* annotators could revise earlier answers
* snippets were shown without needing to be syntactically complete
* annotators provided experience metadata

---

## 🤝 Citation

If you use this dataset, please cite:

```
@inproceedings{dorn2012readability,
  title={A general software readability model},
  author={Dorn, Jonathan and Weimer, Westley},
  booktitle={International Conference on Software Engineering},
  year={2012}
}
```

## 🙌 Acknowledgements

Special thanks to the thousands of anonymous survey respondents, Udacity participants, and reddit programmers who contributed ratings.

---

## Contact

If you have questions or would like to extend the dataset, feel free to open an issue or discussion in the repo.

---