Heap Decomposition for Concurrent Shape Analysis

We demonstrate shape analyses that can achieve a state space reduction exponential in the number of threads compared to the state-of-the-art analyses, while retaining sufficient precision to verify sophisticated properties such as linearizability. The key idea is to abstract the global heap by decomposing it into (not necessarily disjoint) subheaps, abstracting away some correlations between them. These new shape analyses are instances of an analysis framework based on heap decomposition. This framework allows rapid prototyping of complex static analyses by providing efficient abstract transformers given user-specified decomposition schemes. Initial experiments confirm the value of heap decomposition in scaling concurrent shape analyses.

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In  Static Analysis, 15th International Symposium, SAS 2008, Valencia, Spain, July 16-18, 2008. Proceedings

Publisher  Springer Verlag
All copyrights reserved by Springer 2007.

Details

TypeInproceedings
Pages363-377
Volume5079
SeriesLecture Notes in Computer Science
ISBN978-3-540-69163-1

Previous Versions

Roman Manevich, Tal Lev-Ami, Mooly Sagiv, Ganesan Ramalingam, and Josh Berdine. Heap Decomposition for Concurrent Shape Analysis, November 2007.

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