Heap Decomposition for Concurrent Shape Analysis

  • Roman Manevich
  • Tal Lev-Ami
  • Mooly Sagiv
  • Ganesan Ramalingam
  • Josh Berdine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5079)


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.


Shape Analysis Decomposition Scheme Full State Concurrent Program Concurrent Data Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Roman Manevich
    • 1
  • Tal Lev-Ami
    • 1
  • Mooly Sagiv
    • 1
  • Ganesan Ramalingam
    • 2
  • Josh Berdine
    • 3
  1. 1.Tel Aviv University 
  2. 2.Microsoft ResearchIndia
  3. 3.Microsoft Research Cambridge 

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