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Syntax-guided synthesis of Datalog programs

Title
Syntax-guided synthesis of Datalog programs
Author
이우석
Keywords
Active learning; Datalog; Program analysis; Syntax-guided synthesis; Template augmentation
Issue Date
2018-11
Publisher
ACM
Citation
ESEC/FSE 2018 Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Page. 515-527
Abstract
Datalog has witnessed promising applications in a variety of domains. We propose a programming-by-example system, ALPS, to synthesize Datalog programs from input-output examples. Scaling synthesis to realistic programs in this manner is challenging due to the rich expressivity of Datalog. We present a syntax-guided synthesis approach that prunes the search space by exploiting the observation that in practice Datalog programs comprise rules that have similar latent syntactic structure. We evaluate ALPS on a suite of 34 benchmarks from three domains—knowledge discovery, program analysis, and database queries. The evaluation shows that ALPS can synthesize 33 of these benchmarks, and outperforms the state-of-the-art tools Metagol and Zaatar, which can synthesize only up to 10 of the benchmarks.
URI
https://dl.acm.org/citation.cfm?id=3236034https://repository.hanyang.ac.kr/handle/20.500.11754/105784
ISBN
978-1-4503-5573-5
DOI
10.1145/3236024.3236034
Appears in Collections:
COLLEGE OF COMPUTING(소프트웨어융합대학)[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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