NDLI: Approximating inclusion-based points-to analysis

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Content Provider	ACM Digital Library
Author	Nasre, Rupesh
Abstract	It has been established that achieving a points-to analysis that is scalable in terms of analysis time typically involves trading off analysis precsision and/or memory. In this paper, we propose a novel technique to approximate the solution of an inclusion-based points-to analysis. The technique is based on intelligently approximating pointer- and location-equivalence across variables in the program. We develop a simple approximation algorithm based on the technique. By exploiting various behavioral properties of the solution, we develop another improved algorithm which implements various optimizations related to the merging order, proximity search, lazy merging and identification frequency. The improved algorithm provides a strong control to the client to trade off analysis time and precision as per its requirements. Using a large suite of programs including SPEC 2000 benchmarks and five large open source programs, we show how our algorithm helps achieve a scalable solution.
Starting Page	66
Ending Page	73
Page Count	8
File Format	PDF
ISBN	9781450307949
DOI	10.1145/1988915.1988931
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-06-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Approximate analysis Pointer analysis
Content Type	Text
Resource Type	Article

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Approximating inclusion-based points-to analysis