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SDSAT: Tight Integration of Small Domain Encoding and Lazy Approaches in a Separation Logic Solver

  • Malay K Ganai
  • Muralidhar Talupur
  • Aarti Gupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3920)

Abstract

Existing Separation Logic (a.k.a Difference Logic, DL) solvers can be broadly classified as eager or lazy, each with its own merits and de-merits. We propose a novel Separation Logic Solver SDSAT that combines the strengths of both these approaches and provides a robust performance over a wide set of benchmarks. The solver SDSAT works in two phases: allocation and solve. In the allocation phase, it allocates non-uniform adequate ranges for variables appearing in separation predicates. This phase is similar to previous small domain encoding approaches, but uses a novel algorithm Nu-SMOD with 1-2 orders of magnitude improvement in performance and smaller ranges for variables. Furthermore, the Separation Logic formula is not transformed into an equi-satisfiable Boolean formula in one step, but rather done lazily in the following phase. In the solve phase, SDSAT uses a lazy refinement approach to search for a satisfying model within the allocated ranges. Thus, any partially DL-theory consistent model can be discarded if it can not be satisfied within the allocated ranges. Note the crucial difference: in eager approaches, such a partially consistent model is not allowed in the first place, while in lazy approaches such a model is never discarded. Moreover, we dynamically refine the allocated ranges and search for a feasible solution within the updated ranges. This combined approach benefits from both the smaller search space (as in eager approaches) and also from the theory-specific graph-based algorithms (characteristic of lazy approaches). Experimental results show that our method is robust and always better than or comparable to state-of-the art solvers.

Keywords

Boolean Formula Satisfying Assignment Constraint Graph Negative Cycle Strongly Connect Component 
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 2006

Authors and Affiliations

  • Malay K Ganai
    • 1
  • Muralidhar Talupur
    • 2
  • Aarti Gupta
    • 1
  1. 1.NEC LABS AmericaPrincetonUSA
  2. 2.Carnegie Mellon UniversityPittsburghUSA

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