EDBT 2026 Demo / reviewers in the wild / expert
Wang Rui
dblp:350/0631
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0001-0343-7860ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software 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.
| Software engineering, system software, and programming languages
1 paper |
Software testing · 100% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › vulnerability discovery › fuzzing
kernel fuzzing |
0.9 | 1 | 2025 | Snowplow: Effective Kernel Fuzzing with a Learned White-box Test Mutator · ASPLOS (2) 2025 |
Software testing
fuzzing |
0.9 | 1 | 2025 | Snowplow: Effective Kernel Fuzzing with a Learned White-box Test Mutator · ASPLOS (2) 2025 |
Software testing › mutation testing
program mutation |
0.9 | 1 | 2025 | Snowplow: Effective Kernel Fuzzing with a Learned White-box Test Mutator · ASPLOS (2) 2025 |
Software testing › system software testing
kernel testing |
0.3 | 1 | 2025 | Snowplow: Effective Kernel Fuzzing with a Learned White-box Test Mutator · ASPLOS (2) 2025 |
Methods — techniques the papers use, named apart from their topics
white-box mutation · 1.7machine learning · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Snowplow: Effective Kernel Fuzzing with a Learned White-box Test MutatorabstractKernel fuzzers rely heavily on program mutation to automatically generate new test programs based on existing ones. In particular, program mutation can alter the test's control and data flow inside the kernel by inserting new system calls, changing the values of call arguments, or performing other program mutations. However, due to the complexity of the kernel code and its user-space interface, finding the effective mutation that can lead to the desired outcome such as increasing the coverage and reaching a target code location is extremely difficult, even with the widespread use of manually-crafted heuristics. Sishuai Gong, Wang Rui, Deniz Altinbüken, Pedro Fonseca 0001, Petros Maniatis |
ASPLOS (2) | 2 |