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An Approximation Framework for Solvers and Decision Procedures.

Aleksandar Zeljić1, Christoph M Wintersteiger2, Philipp Rümmer1

  • 1Uppsala University, Uppsala, Sweden.

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Summary
This summary is machine-generated.

This study introduces a novel framework for efficiently modeling complex constraints, improving automated test input generation. The method uses flexible approximations for better performance on challenging problems.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • Constraint modeling is crucial for applications like automated test input generation.
  • Naïve encoding of complex constraints into simpler theories leads to performance issues (size, space, runtime).
  • Existing methods struggle with complex background theories like floating-point arithmetic.

Purpose of the Study:

  • To develop a framework for automatically and efficiently computing models of constraints.
  • To address the performance limitations of traditional constraint encoding methods.
  • To handle complex background theories in constraint satisfaction.

Main Methods:

  • A framework for the systematic application of approximations in constraint modeling.
  • Utilizing approximations that are neither under- nor over-approximations.
  • Developing efficient computation methods for constraint models.

Main Results:

  • The proposed method demonstrates promising performance on practically relevant benchmark problems.
  • The framework offers a more general approach compared to previous techniques.
  • Achieved improved efficiency in computing constraint models.

Conclusions:

  • The developed framework enables efficient and automatic constraint modeling, even with complex background theories.
  • The flexible approximation approach enhances performance and applicability.
  • This work contributes to advancing automated reasoning and software testing.