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Qiu-Liang Chen

dblp:170/3323 · DBLP profile ↗
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3ranked-venue papers
1as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1

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
2 papers
Program analysis · 64% Concurrent programming · 24% Operating systems · 12%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis
static analysis
1.022022
Hybrid Static-Dynamic Analysis of Data Races Caused by Inconsistent Locking Discipline in Device Drivers · IEEE Trans. Software Eng. 2022
Effective Static Analysis of Concurrency Use-After-Free Bugs in Linux Device Drivers · USENIX ATC 2019
Concurrent programming › concurrency bug detection
data race detection
0.612022
Hybrid Static-Dynamic Analysis of Data Races Caused by Inconsistent Locking Discipline in Device Drivers · IEEE Trans. Software Eng. 2022
Program analysis
dynamic analysis
0.612022
Hybrid Static-Dynamic Analysis of Data Races Caused by Inconsistent Locking Discipline in Device Drivers · IEEE Trans. Software Eng. 2022
Operating systems › i/o › i/o subsystem
device drivers
0.322022
Hybrid Static-Dynamic Analysis of Data Races Caused by Inconsistent Locking Discipline in Device Drivers · IEEE Trans. Software Eng. 2022
Effective Static Analysis of Concurrency Use-After-Free Bugs in Linux Device Drivers · USENIX ATC 2019

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

taint analysis · 0.6lockset analysis · 0.6hybrid static-dynamic analysis · 0.6
YearPublicationVenuePosition
2022 Hybrid Static-Dynamic Analysis of Data Races Caused by Inconsistent Locking Discipline in Device Drivers
abstract
Data races are often hard to detect in device drivers. According to our study of Linux driver patches that fix data races, about 39% of patches involve a pattern that we callinconsistent locking discipline. Specifically, if a variable is accessed within two concurrently executed functions, the sets of locks held around each access are disjoint, at least one of the locksets is non-empty, and at least one of the involved accesses is a write, then a data race may occur. In this paper, we present a hybrid static-dynamic analysis approach, named SDILP, to detect data races caused by inconsistent locking discipline in device drivers. SDILP has a dynamic lockset analysis to detect data races at runtime, and a static lockset analysis to detect more data races based on the dynamic-analysis results. It also performs a static taint analysis to reduce the number of variable accesses monitored by the dynamic analysis. Compared to our previous dynamic approach DILP (Chen et al., 2019), introducing static analysis allows SDILP to achieve better performance and find more data races. We evaluate SDILP on 12 drivers in Linux 5.4, and find 117 real data races, 50 of which have been confirmed by driver developers.
Jia-Ju Bai, Qiu-Liang Chen, Zu-Ming Jiang, Julia Lawall, Shi-Min Hu 0001
IEEE Trans. Software Eng.2
2019 Effective Static Analysis of Concurrency Use-After-Free Bugs in Linux Device Drivers
Jia-Ju Bai, Julia Lawall, Qiu-Liang Chen, Shi-Min Hu 0001
USENIX ATC3
2019 Detecting Data Races Caused by Inconsistent Lock Protection in Device Drivers
abstract
Data races are often hard to detect in device drivers, due to the non-determinism of concurrent execution. According to our study of Linux driver patches that fix data races, more than 38% of patches involve a pattern that we call inconsistent lock protection. Specifically, if a variable is accessed within two concurrently executed functions, the sets of locks held around each access are disjoint, at least one of the locksets is non-empty, and at least one of the involved accesses is a write, then a data race may occur.In this paper, we present a runtime analysis approach, named DILP, to detect data races caused by inconsistent lock protection in device drivers. By monitoring driver execution, DILP collects the information about runtime variable accesses and executed functions. Then after driver execution, DILP analyzes the collected information to detect and report data races caused by inconsistent lock protection. We evaluate DILP on 12 device drivers in Linux 4.16.9, and find 25 real data races.
Qiu-Liang Chen, Jia-Ju Bai, Zu-Ming Jiang, Julia Lawall, Shi-Min Hu 0001
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