Syntax-guided synthesis of datalog programs
- Authors
- Si, X.; Lee, W.; Zhang, R.; Albarghouthi, A.; Koutris, P.; Naik, M.
- Issue Date
- Oct-2018
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- Active learning; Datalog; Program analysis; Syntax-guided synthesis; Template augmentation
- 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, pp.515 - 527
- Indexed
- OTHER
- Journal Title
- ESEC/FSE 2018 - Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
- Start Page
- 515
- End Page
- 527
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/7903
- DOI
- 10.1145/3236024.3236034
- 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 domainsDknowledge 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. © 2018 Association for Computing Machinery.
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