Accelerating Syntax-Guided Program Synthesis by Optimizing Domain-Specific Languages.

Syntax-guided program synthesis relies on domain-specific languages (DSLs) to constrain the search space and improve efficiency. However, manually designing optimal DSLs is challenging and often results in suboptimal performance. In this paper, we propose AMAZE, a novel framework that automatically optimizes DSLs to accelerate synthesis. AMAZE iteratively refines a DSL by identifying key program fragments, termed feature components, whose enumeration ranks correlate with synthesis time. Using a dynamic-programming-based algorithm to calculate enumeration ranks of feature components and a machine learning model based on them, AMAZE estimates synthesis cost instead of directly invoking the synthesizer, which is impractical due to high computational cost. We evaluate AMAZE on state-of-the-art synthesizers, including DRYADSYNTH, DUET, POLYGEN, and EUsOLVER, across multiple domains. Empirical results demonstrate that AMAZE achieves up to 4.35x speedup, effectively reducing synthesis time while maintaining expressiveness.