Abstract Transformers for Thread Correlation Analysis

  • Michal Segalov
  • Tal Lev-Ami
  • Roman Manevich
  • Ramalingam Ganesan
  • Mooly Sagiv
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5904)


We present a new technique for speeding up static analysis of (shared memory) concurrent programs. We focus on analyses that compute thread correlations: such analyses infer invariants that capture correlations between the local states of different threads (as well as the global state). Such invariants are required for verifying many natural properties of concurrent programs.

Tracking correlations between different thread states, however, is very expensive. A significant factor that makes such analysis expensive is the cost of applying abstract transformers. In this paper, we introduce a technique that exploits the notion of footprints and memoization to compute individual abstract transformers more efficiently.

We have implemented this technique in our concurrent shape analysis framework. We have used this implementation to prove properties of fine-grained concurrent programs with a shared, mutable, heap in the presence of an unbounded number of objects and threads. The properties we verified include memory safety, data structure invariants, partial correctness, and linearizability. Our empirical evaluation shows that our new technique reduces the analysis time significantly (e.g., by a factor of 35 in one case).


Local Store Concurrent Program Program Counter Concrete State Abstract Domain 
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 2009

Authors and Affiliations

  • Michal Segalov
    • 1
  • Tal Lev-Ami
    • 1
  • Roman Manevich
    • 2
  • Ramalingam Ganesan
    • 3
  • Mooly Sagiv
    • 1
  1. 1.Tel Aviv University 
  2. 2.University of California Los Angeles 
  3. 3.Microsoft ResearchIndia

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