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A CLP Method for Compositional and Intermittent Predicate Abstraction

  • Joxan Jaffar
  • Andrew E. Santosa
  • Răzvan Voicu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3855)

Abstract

We present an implementation of symbolic reachability analysis with the features of compositionality, and intermittent abstraction, in the sense of pefrorming approximation only at selected program points, if at all. The key advantages of compositionality are well known, while those of intermittent abstraction are that the abstract domain required to ensure convergence of the algorithm can be minimized, and that the cost of performing abstractions, now being intermittent, is reduced.

We start by formulating the problem in CLP, and first obtain compositionality. We then address two key efficiency challenges. The first is that reasoning is required about the strongest-postcondition operator associated with an arbitrarily long program fragment. This essentially means dealing with constraints over an unbounded number of variables describing the states between the start and end of the program fragment at hand. This is addressed by using the variable elimination or projection mechanism that is implicit in CLP systems. The second challenge is termination, that is, to determine which subgoals are redundant. We address this by a novel formulation of memoization called coinductive tabling.

We finally evaluate the method experimentally. At one extreme, where abstraction is performed at every step, we compare against a model checker. At the other extreme, where no abstraction is performed, we compare against a program verifier. Of course, our method provides for the middle ground, with a flexible combination of abstraction and Hoare-style reasoning with predicate transformers and loop-invariants.

Keywords

Model Checker Proof Obligation Abstract Domain Java Modelling Language Program Point 
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 2005

Authors and Affiliations

  • Joxan Jaffar
    • 1
  • Andrew E. Santosa
    • 1
  • Răzvan Voicu
    • 1
  1. 1.School of ComputingNational University of SingaporeSingaporeRepublic of Singapore

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