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Navindra Umanee

dblp:83/4643 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2003
—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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis › static analysis › pointer analysis
inclusion-based points-to analysis
0.012003
Points-to analysis using BDDs · PLDI 2003
Program analysis › static analysis
pointer analysis
0.012003
Points-to analysis using BDDs · PLDI 2003
Program analysis › static analysis
constraint-based analysis
0.012003
Points-to analysis using BDDs · PLDI 2003
Program analysis
static analysis
0.012003
Points-to analysis using BDDs · PLDI 2003

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

variable ordering · 0.0incremental propagation · 0.0binary decision diagrams · 0.0
YearPublicationVenuePosition
2003 Points-to analysis using BDDs
abstract
This paper reports on a new approach to solving a subset-based points-to analysis for Java using Binary Decision Diagrams (BDDs). In the model checking community, BDDs have been shown very effective for representing large sets and solving very large verification problems. Our work shows that BDDs can also be very effective for developing a points-to analysis that is simple to implement and that scales well, in both space and time, to large programs.The paper first introduces BDDs and operations on BDDs using some simple points-to examples. Then, a complete subset-based points-to algorithm is presented, expressed completely using BDDs and BDD operations. This algorithm is then refined by finding appropriate variable orderings and by making the algorithm propagate sets incrementally, in order to arrive at a very efficient algorithm. Experimental results are given to justify the choice of variable ordering, to demonstrate the improvement due to incrementalization, and to compare the performance of the BDD-based solver to an efficient hand-coded graph-based solver. Finally, based on the results of the BDD-based solver, a variety of BDD-based queries are presented, including the points-to query.
Marc Berndl, Ondrej Lhoták, Laurie J. Hendren, Navindra Umanee
PLDI5