Xingyun Du

dblp:322/7463 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Systems and software security › vulnerability discovery › fuzzing
kernel fuzzing
0.612022
Demystifying the Dependency Challenge in Kernel Fuzzing · ICSE 2022
Systems and software security
vulnerability discovery
0.612022
Demystifying the Dependency Challenge in Kernel Fuzzing · ICSE 2022
Operating systems
kernel
0.212022
Demystifying the Dependency Challenge in Kernel Fuzzing · ICSE 2022

Methods — techniques the papers use, named apart from their topics

fuzz testing · 1.1
YearPublicationVenuePosition
2022 Demystifying the Dependency Challenge in Kernel Fuzzing
abstract
Fuzz 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
ICSE4