# Defectors: A Large, Diverse Python Dataset for Defect Prediction

arXiv: https://arxiv.org/abs/2303.04738 · HTML: https://mlsanity.com/paper/2303.04738/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection](https://mlsanity.com/paper/2505.05777/) (2025)
- [Predicting Line-Level Defects by Capturing Code Contexts with Hierarchical Transformers](https://mlsanity.com/paper/2312.11889/) (2023)

## Abstract (arXiv)

Defect prediction has been a popular research topic where machine learning (ML) and deep learning (DL) have found numerous applications. However, these ML/DL-based defect prediction models are often limited by the quality and size of their datasets. In this paper, we present Defectors, a large dataset for just-in-time and line-level defect prediction. Defectors consists of ≈ 213K source code files (≈ 93K defective and ≈ 120K defect-free) that span across 24 popular Python projects. These projects come from 18 different domains, including machine learning, automation, and internet-of-things. Such a scale and diversity make Defectors a suitable dataset for training ML/DL models, especially transformer models that require large and diverse datasets. We also foresee several application areas of our dataset including defect prediction and defect explanation. Dataset link: https://doi.org/10.5281/zenodo.7708984