研究数据增强如何在训练和测试中引入偏差，揭示其对模型评估的影响。
Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models
- 通过案例分析评估数据增强在训练与测试中引发的偏差
- 发现测试集使用增强样本会显著扭曲模型性能评估结果
- 适合关注模型可靠性与测试数据质量的研究者
数据增强已成为软件工程中应对数据稀缺或不平衡的标准方法，尤其在测试分类和缺陷检测等专业领域。尽管SMOTE和基于变异的增强技术被广泛应用于软件测试与调试，但关于增强数据如何影响模型偏差的系统性理解仍不足。特别值得注意的是，当增强数据不仅用于训练，也用于测试时，其对模型评估的影响尤为关键。本文通过对翻车测试（flaky test）分类的全面案例研究，提出了一种检测偏差的方法，并揭示了在测试集中引入增强样本会对模型评估产生显著影响。
原文摘要 · Abstract (English)
Data augmentation has become a standard practice in software engineering to address limited or imbalanced data sets, particularly in specialized domains like test classification and bug detection where data can be scarce. Although techniques such as SMOTE and mutation-based augmentation are widely used in software testing and debugging applications, a rigorous understanding of how augmented training data impacts model bias is lacking. It is especially critical to consider bias in scenarios where augmented data sets are used not just in training but also in testing models. Through a comprehensive case study of flaky test classification, we demonstrate how to test for bias and understand the impact that the inclusion of augmented samples in testing sets can have on model evaluation.
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