Defectors: A Large, Diverse Python Dataset for Defect Prediction

Abstract

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

References in corpus (5)

• Do We Need More Training Data?
• CC2Vec: Distributed Representations of Code Changes
• Predicting Defective Lines Using a Model-Agnostic Technique
• Explaining Software Bugs Leveraging Code Structures in Neural Machine Translation
• Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction