Syntax-Guided Synthesis

Rajeev Alur, Rastislav Bodik, Eric Dallal, Dana Fisman, Pranav Garg, Garvit Juniwal, Hadas Kress-Gazit, P. Madhusudan, Milo M. K. Martin, Mukund Raghothaman, Shamwaditya Saha, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, Abhishek Udupa

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The classical formulation of the program-synthesis problem is to find a
program that meets a correctness specification given as a logical formula. Recent work on program synthesis and program optimization illustrates many potential benefits of allowing the user to supplement the logical specification with a syntactic template that constrains the space of allowed implementations. Our goal is to identify the core computational problem common to these proposals in a logical framework. The input to the syntax-guided synthesis problem (SyGuS) consists of a background theory, a semantic correctness specification for the desired program given by a logical formula, and a syntactic set of candidate implementations given by a grammar. The computational problem then is to find an implementation from the set of candidate expressions so that it satisfies the specification in the given theory. We describe alternative solution strategies that combine learning, counter example analysis and constraint solving. We report on prototype implementations, and present experimental results on the set of benchmarks collected as part of the
first SyGuS-Comp competition held in July 2014.
Original languageEnglish
StatePublished - 2015

Publication series

NameNato Science for Peace and Security Series D-Information and Communication Security


  • Constraint Solving
  • Counterexamples
  • Machine Learning
  • Program Synthesis


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