Heap decomposition for concurrent shape analysis

Roman Manevich, Tal Lev-Ami, Mooly Sagiv, Ganesan Ramalingam, Josh Berdine

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


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.

Original languageEnglish
Title of host publicationStatic Analysis - 15th International Symposium, SAS 2008, Proceedings
PublisherSpringer Verlag
Number of pages15
ISBN (Print)3540691634, 9783540691631
StatePublished - 1 Jan 2008
Externally publishedYes
Event15th International Static Analysis Symposium, SAS 2008 - Valencia, Spain
Duration: 16 Jul 200818 Jul 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5079 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Static Analysis Symposium, SAS 2008

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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