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 4.35× speedup, effectively reducing synthesis time while maintaining expressiveness.

@article{paperbot3909,
  title = {Accelerating Syntax-Guided Program Synthesis by Optimizing Domain-Specific Languages},
  author = {Zhentao Ye and Ruyi Ji and Yingfei Xiong and Xin Zhang},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {10},
  number = {POPL},
  year = {2026},
  doi = {10.1145/3776679}
}