Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Proceedings of the ACM on Programming Languages

DOI: 10.1145/3776679

Authors: Zhentao Ye, Ruyi Ji, Yingfei Xiong, Xin Zhang

Institution: Peking University

Language: English

Topic: Evolutionary Algorithms and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Awards: G4869734229, G5212030523, G5629752981