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Ziyun Kong

dblp:145/0977 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

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

Software engineering, systems software and programming languages · 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
1 paper
Concurrent programming · 67% Program analysis · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Concurrent programming
concurrency bug detection
0.212014
Race detection for event-driven mobile applications · PLDI 2014
Concurrent programming › concurrency bug detection
data race detection
0.212014
Race detection for event-driven mobile applications · PLDI 2014
Program analysis › concurrent program analysis
event-race detection
0.212014
Race detection for event-driven mobile applications · PLDI 2014

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

dynamic race detection · 0.2
YearPublicationVenuePosition
2014 Race detection for event-driven mobile applications
abstract
Mobile systems commonly support an event-based model of concurrent programming. This model, used in popular platforms such as Android, naturally supports mobile devices that have a rich array of sensors and user input modalities. Unfortunately, most existing tools for detecting concurrency errors of parallel programs focus on a thread-based model of concurrency. If one applies such tools directly to an event-based program, they work poorly because they infer false dependencies between unrelated events handled sequentially by the same thread.
Chun-Hung Hsiao, Cristiano Pereira, Jie Yu 0016, Gilles Pokam, Satish Narayanasamy, Peter M. Chen, Ziyun Kong, Jason Flinn
PLDI7