Synthesizing imperative programs from examples guided by static analysis

Abstract

We present a novel algorithm for efficiently synthesizing imperative programs from examples. Given a set of input-output examples and a partial program, our algorithm generates a complete program that is consistent with every example. Our algorithm is based on enumerative synthesis, which explores all candidate programs in increasing size until it finds a solution. This algorithm, however, is too slow to be used in practice. Our key idea to accelerate the speed is to perform static analysis alongside the enumerative search, in order to “statically” identify and safely prune out partial programs that eventually fail to be a solution. We have implemented our algorithm in a tool, SIMPL, and evaluated it on 30 introductory programming problems gathered from online forums. The results show that our static analysis approach improves the speed of enumerative synthesis by 25x on average.