一句话：针对软件工程数据中标签噪声与类别不平衡问题，研究深度预测模型的鲁棒学习，以提升缺陷预测与 issue 分类等任务效果。
随着深度学习快速发展，深度预测模型已广泛用于缺陷预测、issue 分类等软件工程任务并取得显著成功。这些模型多采用监督学习，高度依赖高质量数据集。然而，由于软件工程数据的性质和来源，真实数据集常存在样本误标注与类别不平衡问题，从而削弱深度预测模型的实际效果，该问题已成为基于深度学习的软件工程的主要障碍。论文标题表明其关注从噪声和不平衡软件工程数据集中鲁棒学习深度预测模型，但摘要未具体说明所采用的方法、关键结果与实验评估细节。
With the rapid development of Deep Learning, deep predictive models have been widely applied to improve Software Engineering tasks, such as defect prediction and issue classification, and have achieved remarkable success. They are mostly trained in a supervised manner, which heavily relies on high-quality datasets. Unfortunately, due to the nature and source of software engineering data, the real-world datasets often suffer from the issues of sample mislabelling and class imbalance, thus undermining the effectiveness of deep predictive models in practice. This problem has become a major obstacle for deep learning-based Software Engineering.
@inproceedings{paperbot7305,
title = {Robust Learning of Deep Predictive Models from Noisy and Imbalanced Software Engineering Datasets},
author = {Zhong Li and Minxue Pan and Yu Pei and Tian Zhang and Linzhang Wang and Xuandong Li},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3556941}
}