一句话：提出 Raisin，旨在识别调用极少的稀有敏感函数，以支撑缺陷检测。
论文关注缺陷检测中的敏感函数识别问题。已有基于机器学习或自然语言处理/统计的自动化技术能够在大规模软件系统中识别易出错的敏感函数，但这类技术难以处理调用次数极少的稀有敏感函数；而稀有敏感函数同样可能引入缺陷。为此，论文提出 Raisin，目标是在该设定下有效识别稀有敏感函数，从而辅助缺陷检测。摘要仅阐述问题与动机，未给出 Raisin 的具体方法、实现细节和实验评估结果。
Mastering the knowledge about the bug-prone functions (i.e., sensitive functions) is important to detect bugs. Some automated techniques have been proposed to identify the sensitive functions in large software systems, based on machine learning or natural language processing. However, the existing statistics-based techniques are not directly applicable to a special kind of sensitive functions, i.e., the rare sensitive functions, which have very few invocations even in large systems. Unfortunately, the rare ones can also introduce bugs. Therefore, how to effectively identify such functions is a problem deserving attention.
@inproceedings{paperbot11449,
title = {Raisin: Identifying Rare Sensitive Functions for Bug Detection},
author = {Jianjun Huang and Jianglei Nie and Yuanjun Gong and Wei You and Bin Liang and Pan Bian},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639165}
}