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.
Thu 8 Nov
|10:30 - 10:52|
Kevin MoranCollege of William & Mary, Carlos Bernal-CárdenasWilliam and Mary, Michael Curcio, Richard Bonett, Denys PoshyvanykWilliam and MaryDOI Pre-print Media Attached
|10:52 - 11:15|
|11:15 - 11:37|
Andrea StoccoUniversity of British Columbia, Rahulkrishna YandrapallyUniversity of British Columbia, Canada, Ali MesbahUniversity of British ColumbiaPre-print Media Attached
|11:37 - 12:00|