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Synthesising Programs with Non-trivial Constants.

Alessandro Abate1, Haniel Barbosa2, Clark Barrett3

  • 1University of Oxford, Oxford, UK.

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Summary
This summary is machine-generated.

This study introduces CEGIS(), a novel program synthesis approach combining inductive synthesis with theory solving. It efficiently generates programs with complex constants, improving automated software construction.

Keywords:
Automated reasoningCounterexample guided inductive synthesisProgram synthesisSatisfiability modulo theories

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Program synthesis aims to automate software construction.
  • Current methods struggle with generating programs containing non-trivial constants.
  • Existing tools often require user-defined syntactic restrictions, limiting flexibility.

Purpose of the Study:

  • To develop a more efficient program synthesis method for generating programs with non-trivial constants.
  • To enhance automated software construction by overcoming limitations of existing techniques.
  • To explore the integration of counterexample-guided inductive synthesis with theory solving.

Main Methods:

  • Proposed a new approach named CEGIS(), integrating counterexample-guided inductive synthesis with first-order theory solvers.
  • Developed two exemplars of CEGIS(): one using Fourier-Motzkin (FM) variable elimination and another based on first-order satisfiability.
  • Integrated the CEGIS() approach into the CVC4 synthesizer.

Main Results:

  • CEGIS() efficiently explores the solution space for program synthesis without user-guided syntactic restrictions.
  • Successfully synthesized programs for a range of intricate benchmarks.
  • Demonstrated improved performance when CEGIS() was integrated into the CVC4 synthesizer.

Conclusions:

  • CEGIS() offers a powerful and efficient method for synthesizing programs with non-trivial constants.
  • The approach enhances automated software generation capabilities.
  • Integration with existing tools like CVC4 yields significant performance benefits.