Defectors：用于缺陷预测的大型多样化 Python 数据集
Defectors: A Large, Diverse Python Dataset for Defect Prediction
AI总结：
本文构建涵盖24个Python项目、约213K代码文件的多样化数据集Defectors，以支持即时与行级缺陷预测及ML/DL模型训练。
摘要：
缺陷预测一直是一个热门研究课题，机器学习（ML）和深度学习（DL）已在其中得到大量应用。然而，这些基于 ML/DL 的缺陷预测模型往往受到数据集质量和规模的限制。本文提出 Defectors，这是一个用于即时（just-in-time）和行级缺陷预测的大型数据集。Defectors 包含约 213K 个源代码文件（约 93K 个有缺陷、约 120K 个无缺陷），覆盖 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。这样的规模和多样性使 Defectors 成为训练 ML/DL 模型的合适数据集，尤其是需要大型多样化数据集的 transformer 模型。我们还预见了该数据集的若干应用领域，包括缺陷预测和缺陷解释。数据集链接：https://doi.org/10.5281/zenodo.7708984
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
