Thu 21 Jun 2018 14:50 - 15:15 at Grand Ballroom CD - Synthesis and Learning Chair(s): Xin Zhang

A key challenge in program synthesis concerns how to efficiently search for the desired program in the space of possible programs. We propose a general approach to accelerate search-based program synthesis by biasing the search towards likely programs. Our approach targets a standard formulation, syntax-guided synthesis (SyGuS), by extending the grammar of possible programs with a probabilistic model dictating the likelihood of each program. We develop a weighted search algorithm to efficiently enumerate programs in order of their likelihood. We also propose a method based on transfer learning that enables to effectively learn a powerful model, called probabilistic higher-order grammar, from known solutions in a domain. We have implemented our approach in a tool called Euphony and evaluate it on SyGuS benchmark problems from a variety of domains. We show that Euphony can learn good models using easily obtainable solutions, and achieves significant performance gains over existing general-purpose as well as domain-specific synthesizers.

Thu 21 Jun

pldi-2018-papers
14:00 - 15:40: PLDI Research Papers - Synthesis and Learning at Grand Ballroom CD
Chair(s): Xin ZhangMassachusetts Institute of Technology, USA
pldi-2018-papers14:00 - 14:25
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Uri AlonTechnion, Meital ZilbersteinTechnion, Omer LevyUniversity of Washington, USA, Eran YahavTechnion
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pldi-2018-papers14:25 - 14:50
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Yu FengUniversity of Texas at Austin, USA, Ruben MartinsCarnegie Mellon University, Osbert BastaniStanford University, Isil DilligUT Austin
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pldi-2018-papers14:50 - 15:15
Talk
Woosuk LeeUniversity of Pennsylvania, USA, Kihong HeoUniversity of Pennsylvania, USA, Rajeev AlurUniversity of Pennsylvania, Mayur NaikUniversity of Pennsylvania
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pldi-2018-papers15:15 - 15:40
Talk
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