An empirical study of adaptive concretization for parallel program synthesis

Abstract

Adaptive concretization is a program synthesis technique that enables efficient parallelization of challenging synthesis problems. The key observation behind adaptive concretization is that in a challenging synthesis problem, there are some unknowns that are best suited for explicit search and some that are best suited for symbolic search through constraint solving. At a high level, the main idea behind adaptive concretization is to dynamically identify which unknowns are best suited to which kind of search, and to parallelize the explicit search on those unknowns for which that style of search is more suitable. We first introduced adaptive concretization in an earlier paper [Jeon et al. in Computer aided verification, Springer, Berlin 2015]. Our original algorithm involved a few arbitrary design decisions, leaving open the question of whether different choices could achieve better performance. In this paper, we systematically evaluate several dimensions of the design space to better understand the tradeoffs. We show that, in general, adaptive concretization is robust along those dimensions, and our initial choices were reasonable.