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- W2884889320 abstract "Automatic program synthesis promises to increase the productivity of programmers and end-users of computing devices by automating tedious and error-prone tasks. Despite the practical successes of program synthesis, we still do not have systematic frameworks to synthesize programs that are “good” according to certain metrics—e.g., produce programs of reasonable sizes or with good runtime—and to understand when synthesis can result in such good programs. In this paper, we propose QSyGuS, a unifying framework for describing syntax-guided synthesis problems with quantitative objectives over the syntax of the synthesized programs. QSyGuS builds on weighted (tree) grammars, a clean and foundational formalism that provides flexible support for different quantitative objectives, useful closure properties, and practical decision procedures. We then present an algorithm for solving QSyGuS. Our algorithm leverages closure properties of weighted grammars to generate intermediate problems that can be solved using non-quantitative SyGuS solvers. Finally, we implement our algorithm in a tool, QuaSi, and evaluate it on 26 quantitative extensions of existing SyGuS benchmarks. QuaSi can synthesize optimal solutions in 15/26 benchmarks with times comparable to those needed to find an arbitrary solution." @default.
- W2884889320 created "2018-08-03" @default.
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- W2884889320 date "2018-01-01" @default.
- W2884889320 modified "2023-10-16" @default.
- W2884889320 title "Syntax-Guided Synthesis with Quantitative Syntactic Objectives" @default.
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- W2884889320 doi "https://doi.org/10.1007/978-3-319-96145-3_21" @default.
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