一句话：用配置采样与自动特征识别模拟可变性感知分析，在三个可配置C项目上发现80%警告为可变性警告，人工确认77个真实缺陷。
许多关键C软件系统使用编译时可配置性，大量配置组合使静态分析难以有效检测缺陷，而已有的可变性感知分析与先进缺陷检测工具间仍有差距。本文提出利用配置采样、现成的“可变性无关”缺陷检测器及自动特征识别技术来模拟可变性感知分析。作者在三个高度可配置的真实C项目上，用四种流行静态分析工具获得36,061条警告，80%为可变性警告。分析发现大多数警告是各类可变性警告（如NULL解引用）。人工调查后产出包含77个已确认真实缺陷（其中52个为可变性缺陷）的基准，可用于未来可变性感知分析的开发。
Many critical software systems developed in C utilize compile-time configurability. The many possible configurations of this software make bug detection through static analysis difficult. While variability-aware static analyses have been developed, there remains a gap between those and state-of-the-art static bug detection tools. In order to collect data on how such tools may perform and to develop real-world benchmarks, we present a way to leverage configuration sampling, off-the-shelf “variability-oblivious” bug detectors, and automatic feature identification techniques to simulate a variability-aware analysis. We instantiate our approach using four popular static analysis tools on three highly configurable, real-world C projects, obtaining 36,061 warnings, 80% of which are variability warnings. We analyze the warnings we collect from these experiments, finding that most results are variability warnings of a variety of kinds such as NULL dereference. We then manually investigate these warnings to produce a benchmark of 77 confirmed true bugs (52 of which are variability bugs) useful for future development of variability-aware analyses.
@inproceedings{paperbot5093,
title = {An empirical study of real-world variability bugs detected by variability-oblivious tools},
author = {Austin Mordahl and Jeho Oh and Ugur Koc and Shiyi Wei and Paul Gazzillo},
booktitle = {FSE},
year = {2019},
doi = {10.1145/3338906.3338967}
}