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Approximations for Model Construction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8562)

Abstract

We consider the problem of efficiently computing models for satisfiable constraints, in the presence of complex background theories such as floating-point arithmetic. Model construction has various applications, for instance the automatic generation of test inputs. It is well-known that naive encoding of constraints into simpler theories (for instance, bit-vectors or propositional logic) can lead to a drastic increase in size, and be unsatisfactory in terms of memory and runtime needed for model construction. We define a framework for systematic application of approximations in order to speed up model construction. Our method is more general than previous techniques in the sense that approximations that are neither under- nor over-approximations can be used, and shows promising results in practice.

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Uppsala UniversitySweden
  2. 2.Microsoft ResearchUK

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