EDBT 2026 Demo / reviewers in the wild / expert
Xingyun Du
dblp:322/7463
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Systems and software security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Operating systems · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › vulnerability discovery › fuzzing
kernel fuzzing |
0.6 | 1 | 2022 | Demystifying the Dependency Challenge in Kernel Fuzzing · ICSE 2022 |
Systems and software security
vulnerability discovery |
0.6 | 1 | 2022 | Demystifying the Dependency Challenge in Kernel Fuzzing · ICSE 2022 |
Operating systems
kernel |
0.2 | 1 | 2022 | Demystifying the Dependency Challenge in Kernel Fuzzing · ICSE 2022 |
Methods — techniques the papers use, named apart from their topics
fuzz testing · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Demystifying the Dependency Challenge in Kernel FuzzingabstractFuzz testing operating system kernels remains a daunting task to date. One known challenge is that much of the kernel code is locked under specific kernel states and current kernel fuzzers are not effective in exploring such an enormous state space. We refer to this problem as the dependency challenge. Though there are some efforts trying to address the dependency challenge, the prevalence and categorization of dependencies have never been studied. Most prior work simply attempted to recover dependencies opportunistically whenever they are relatively easy to recognize. In this paper, we undertake a substantial measurement study to systematically understand the real challenge behind dependencies. To our surprise, we show that even for well-fuzzed kernel modules, unresolved dependencies still account for 59% - 88% of the uncovered branches. Furthermore, we show that the dependency challenge is only a symptom rather than the root cause of failing to achieve more coverage. By distilling and summarizing our findings, we believe the research provides valuable guidance to future research in kernel fuzzing. Finally, we propose a number of novel research directions directly based on the insights gained from the measurement study. Yu Hao 0006, Hang Zhang 0012, Guoren Li, Xingyun Du, Zhiyun Qian, Ardalan Amiri Sani |
ICSE | 4 |