Efficient Synthesis with Probabilistic Constraints

05/20/2019
by   Samuel Drews, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/15/2015

A Theory of Formal Synthesis via Inductive Learning

Formal synthesis is the process of generating a program satisfying a hig...
research
09/07/2018

Relational Program Synthesis

This paper proposes relational program synthesis, a new problem that con...
research
10/24/2021

Scaling Neural Program Synthesis with Distribution-based Search

We consider the problem of automatically constructing computer programs ...
research
06/13/2006

On the Efficiency of Strategies for Subdividing Polynomial Triangular Surface Patches

In this paper, we investigate the efficiency of various strategies for s...
research
01/26/2023

Synthesizing Specifications

Every program should always be accompanied by a specification that descr...
research
06/23/2022

Algebra-Based Reasoning for Loop Synthesis

Provably correct software is one of the key challenges of our software-d...
research
03/30/2022

Type-Directed Program Synthesis for RESTful APIs

With the rise of software-as-a-service and microservice architectures, R...

Please sign up or login with your details

Forgot password? Click here to reset