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High-throughput Synthesis of Carbohydrates and Functionalization of Polyanhydride Nanoparticles
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Program synthesis: challenges and opportunities.

Cristina David1, Daniel Kroening2

  • 1Department of Computer Science, University of Oxford, Oxford, UK.

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Summary
This summary is machine-generated.

Program synthesis automates software creation by generating code from specifications. Counter-example-guided inductive synthesis (CEGIS) is a key technique driving new tools and broader applications.

Keywords:
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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Program synthesis automates software development, generating code from high-level specifications.
  • This field aims to relieve programmers from implementation details, focusing on problem description.
  • Synthesis tools act as solvers for second-order existential logic.

Purpose of the Study:

  • To survey applications and recent trends in program synthesis algorithms.
  • To highlight counter-example-guided inductive synthesis (CEGIS) as a pivotal advancement.
  • To describe the CEGIS architecture and recent algorithmic improvements.

Main Methods:

  • Review of existing program synthesis techniques and applications.
  • Focus on counter-example-guided inductive synthesis (CEGIS) methodology.
  • Analysis of algorithmic trends and improvements within CEGIS.

Main Results:

  • Program synthesis offers a broad range of applications due to its logical foundations.
  • Counter-example-guided inductive synthesis (CEGIS) has significantly advanced the field.
  • Recent algorithmic improvements enhance the capabilities of synthesis engines.

Conclusions:

  • Program synthesis, particularly CEGIS, is a rapidly evolving field with increasing potential.
  • Future advancements in synthesis engines are expected to be transparent to applications.
  • This technology promises to unlock a wider array of use-cases for automated software creation.