一句话：提出 RegMiner，从代码演化历史自动构建大规模回归数据集，以克服人工 bug 数据集的扩展性与代表性瓶颈。
当前缺陷数据集为故障定位、软件测试、程序修复等 SE/PL 研究提供了重要的经验与实验基础，但知名数据集多由人工构建，导致可扩展性、代表性和对新兴数据驱动研究的支持受限。为此，论文提出 RegMiner，旨在从代码演化历史中构建大规模回归数据集。摘要未展开具体挖掘流程、算法细节与实验评估，其核心动机是通过利用版本历史中的代码变更信息，扩大回归数据的来源与规模，从而为数据驱动的软件工程与程序语言研究提供更可扩展、更具代表性的数据基础。
Bug datasets lay significant empirical and experimental foundation for various SE/PL researches such as fault localization, software testing, and program repair. Current well-known datasets are constructed manually, which inevitably limits their scalability, representativeness, and the support for the emerging data-driven research.
@inproceedings{paperbot7430,
title = {RegMiner: towards constructing a large regression dataset from code evolution history},
author = {Xuezhi Song and Yun Lin and Siang Hwee Ng and Yijian Wu and Xin Peng and Jin Song Dong and Hong Mei},
booktitle = {ISSTA},
year = {2022},
doi = {10.1145/3533767.3534224}
}