Adding relative indexes, reusable and optional rule parts, bidirectional search, and simplicity tie-breaking raises the coverage of the best discovered transformation by up to about 10% on the authors' benchmarks.
SyGuS-Comp 2016: Results and Analysis
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abstract
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula $\varphi$ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in SyGuS-IF, a language that is built on top of SMT-LIB. The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In this year's competition we added a new track devoted to programming by examples. This track consisted of two categories, one using the theory of bit-vectors and one using the theory of strings. This paper presents and analyses the results of SyGuS-Comp'16.
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GXJoin: Generalized Cell Transformations for Explainable Joinability
Adding relative indexes, reusable and optional rule parts, bidirectional search, and simplicity tie-breaking raises the coverage of the best discovered transformation by up to about 10% on the authors' benchmarks.