一句话：报告在软件缺陷预测中复用并扩展 Buda 等人的 CNN 类别不平衡过采样方法。
本文是一则方法复用证据记录。其核心是：Buda 等人曾系统研究卷积神经网络中的类别不平衡问题，并提出过采样技术；Yedida 与 Menzies 在 TSE ’21 论文中将该过采样方法及其基础变体进一步应用于软件工程领域，具体用于软件缺陷预测，并对原方法进行了扩展。该工作并未提出新的学习算法，而是记录、确认并说明这一跨领域复用的来源、方式与扩展点。摘要未报告具体实验指标或新评估结果，因此其主要贡献在于提供可追溯的复用证据，帮助理解类别不平衡处理技术如何从 CNN 研究迁移到软件工程缺陷预测任务中。
We report here the reuse of oversampling, and modifications to the basic approach, used in a recent TSE ’21 paper by YedidaMenzies. The method reused is the oversampling technique studied by Buda et al. These methods were studied in the SE domain (specifically, for defect prediction), and extended by Yedida & Menzies.
DOI 原文 ·
@inproceedings{paperbot6437,
title = {Documenting evidence of a reuse of ‘a systematic study of the class imbalance problem in convolutional neural networks’},
author = {Rahul Yedida and Tim Menzies},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3477212}
}