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 $\approx$ 213K source code files ($\approx$ 93K defective and $\approx$ 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

翻译：缺陷预测一直是机器学习（ML）和深度学习（DL）广泛应用的热门研究课题。然而，这些基于ML/DL的缺陷预测模型常常受到数据集质量和规模的限制。本文提出了Defectors，一个用于即时缺陷预测和行级缺陷预测的大规模数据集。Defectors包含约213K个源代码文件（约93K个有缺陷文件和约120K个无缺陷文件），涵盖24个流行的Python项目。这些项目来自18个不同领域，包括机器学习、自动化和物联网。如此的规模和多样性使Defectors成为训练ML/DL模型（特别是需要大规模多样化数据集的Transformer模型）的理想数据集。我们还预见了该数据集的多个应用领域，包括缺陷预测和缺陷解释。数据集链接：https://doi.org/10.5281/zenodo.7708984