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Kwok-Chiang Andrew Chung

dblp:06/9033 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2011
—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
Program analysis · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis › data flow analysis
flow-sensitive analysis
0.112011
Points-to analysis with efficient strong updates · POPL 2011
Program analysis › static analysis
pointer analysis
0.112011
Points-to analysis with efficient strong updates · POPL 2011

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

subset-based analysis · 0.1LLVM · 0.1
YearPublicationVenuePosition
2011 Points-to analysis with efficient strong updates
abstract
This paper explores a sweet spot between flow-insensitive and flow-sensitive subset-based points-to analysis. Flow-insensitive analysis is efficient: it has been applied to million-line programs and even its worst-case requirements are quadratic space and cubic time. Flow-sensitive analysis is precise because it allows strong updates, so that points-to relationships holding in one program location can be removed from the analysis when they no longer hold in other locations. We propose a "Strong Update" analysis combining both features: it is efficient like flow-insensitive analysis, with the same worst-case bounds, yet its precision benefits from strong updates like flow-sensitive analysis. The key enabling insight is that strong updates are applicable when the dereferenced points-to set is a singleton, and a singleton set is cheap to analyze. The analysis therefore focuses flow sensitivity on singleton sets. Larger sets, which will not lead to strong updates, are modelled flow insensitively to maintain efficiency. We have implemented and evaluated the analysis as an extension of the standard flow-insensitive points-to analysis in the LLVM compiler infrastructure.
Ondrej Lhoták, Kwok-Chiang Andrew Chung
POPL2