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arXiv · 1905.08364

Efficient Synthesis with Probabilistic Constraints

Samuel Drews·Aws Albarghouthi·Loris D'Antoni
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Abstract

We consider the problem of synthesizing a program given a probabilistic specification of its desired behavior. Specifically, we study the recent paradigm of distribution-guided inductive synthesis (DIGITS), which iteratively calls a synthesizer on finite sample sets from a given distribution. We make theoretical and algorithmic contributions: (i) We prove the surprising result that DIGITS only requires a polynomial number of synthesizer calls in the size of the sample set, despite its ostensibly exponential behavior. (ii) We present a property-directed version of DIGITS that further reduces the number of synthesizer calls, drastically improving synthesis performance on a range of benchmarks.

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Samuel Drews, Aws Albarghouthi, Loris D'Antoni. 2019-05-20. Efficient Synthesis with Probabilistic Constraints. https://arxiv.org/abs/1905.08364

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