2024/05/27 by Janis Zenkner, Zenkner, Janis, Lukas Dierkes +5 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Embedded Systems Design Techniques #FOS: Computer and information sciences #Parallel Computing and Optimization Techniques #Programming Languages (cs.PL) #Software Engineering (cs.SE) #Teaching and Learning Programming
paper · pdf · doi:10.48550/arxiv.2405.17514
openalex publication_date 2024/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
LambdaBeam is a state-of-the-art, execution-guided algorithm for program synthesis that utilizes higher-order functions, lambda functions, and iterative loops within a Domain-Specific Language (DSL). LambdaBeam generates each program from scratch but does not take advantage of the frequent recurrence of program blocks or subprograms commonly found in specific domains, such as loops for list traversal. To address this limitation, we introduce AbstractBeam: a novel program synthesis framework designed to enhance LambdaBeam by leveraging Library Learning. AbstractBeam identifies and integrates recurring program structures into the DSL, optimizing the synthesis process. Our experimental evaluations demonstrate that AbstractBeam statistically significantly (p < 0.05) outperforms LambdaBeam in the integer list manipulation domain. Beyond solving more tasks, AbstractBeam's program synthesis is also more efficient, requiring less time and fewer candidate programs to generate a solution. Furthermore, our findings indicate that Library Learning effectively enhances program synthesis in domains that are not explicitly designed to showcase its advantages, thereby highlighting the broader applicability of Library Learning.