Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kunpeng Yu
dblp:227/4081
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 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 |
Concurrent programming · 87% Program analysis · 13% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security
memory safety |
0.5 | 1 | 2021 | Detecting concurrency vulnerabilities based on partial orders of memory and thread events · ESEC/SIGSOFT FSE 2021 |
Concurrent programming
concurrency bug detection |
0.5 | 1 | 2021 | Detecting concurrency vulnerabilities based on partial orders of memory and thread events · ESEC/SIGSOFT FSE 2021 |
Concurrent programming › concurrency bug detection
data race detection |
0.5 | 1 | 2021 | Detecting concurrency vulnerabilities based on partial orders of memory and thread events · ESEC/SIGSOFT FSE 2021 |
Program analysis
static analysis |
0.1 | 1 | 2021 | Detecting concurrency vulnerabilities based on partial orders of memory and thread events · ESEC/SIGSOFT FSE 2021 |
Methods — techniques the papers use, named apart from their topics
partial order analysis · 1.0feasible trace construction · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Detecting concurrency vulnerabilities based on partial orders of memory and thread eventsabstractMemory vulnerabilities are the main causes of software security problems. However, detecting vulnerabilities in multi-threaded programs is challenging because many vulnerabilities occur under specific executions, and it is hard to explore all possible executions of a multi-threaded program. Existing approaches are either computationally intensive or likely to miss some vulnerabilities due to the complex thread interleaving. This paper introduces a novel approach to detect concurrency memory vulnerabilities based on partial orders of events. A partial order on a set of events represents the definite execution orders of events. It allows constructing feasible traces exposing specific vulnerabilities by exchanging the execution orders of vulnerability-potential events. It also reduces the search space of possible executions and thus improves computational efficiency. We propose new algorithms to extract vulnerability-potential event pairs for three kinds of memory vulnerabilities. We also design a novel algorithm to compute a potential event pair's feasible set, which contains the relevant events required by a feasible trace. Our method extends existing approaches for data race detection by considering that two events are protected by the same lock. We implement a prototype of our approach and conduct experiments to evaluate its performance. Experimental results show that our tool exhibits superiority over state-of-the-art algorithms in both effectiveness and efficiency. Kunpeng Yu, Chenxu Wang 0001, Yan Cai 0001, Xiapu Luo, Zijiang Yang 0006 |
ESEC/SIGSOFT FSE | 1 |