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Kunpeng Yu

dblp:227/4081 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Systems and software security
memory safety
0.512021
Detecting concurrency vulnerabilities based on partial orders of memory and thread events · ESEC/SIGSOFT FSE 2021
Concurrent programming
concurrency bug detection
0.512021
Detecting concurrency vulnerabilities based on partial orders of memory and thread events · ESEC/SIGSOFT FSE 2021
Concurrent programming › concurrency bug detection
data race detection
0.512021
Detecting concurrency vulnerabilities based on partial orders of memory and thread events · ESEC/SIGSOFT FSE 2021
Program analysis
static analysis
0.112021
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
YearPublicationVenuePosition
2021 Detecting concurrency vulnerabilities based on partial orders of memory and thread events
abstract
Memory 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 FSE1