Accelerating search-based program synthesis using learned probabilistic models

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Abstract

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.

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APA

Lee, W., Heo, K., Alur, R., & Naik, M. (2018). Accelerating search-based program synthesis using learned probabilistic models. In Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) (pp. 436–449). Association for Computing Machinery. https://doi.org/10.1145/3296979.3192410

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