Bayesian Sketch Learning for Program Synthesis

  title={Bayesian Sketch Learning for Program Synthesis},
  author={Vijayaraghavan Murali and Swarat Chaudhuri and Chris Jermaine},
We present a data-driven approach to the problem of inductive computer program synthesis. Our method learns a probabilistic model for realworld programs from a corpus of existing code. It uses this model during synthesis to automatically infer a posterior distribution over sketches, or syntactic models of the problem to be synthesized. Sketches sampled from this posterior are then used to drive combinatorial synthesis of a program in a high-level programming language. The key technical… CONTINUE READING
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