Top-Down Synthesis for Library Learning

This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern ma...

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Vydané v:Proceedings of ACM on programming languages Ročník 7; číslo POPL; s. 1182 - 1213
Hlavní autori: Bowers, Matthew, Olausson, Theo X., Wong, Lionel, Grand, Gabriel, Tenenbaum, Joshua B., Ellis, Kevin, Solar-Lezama, Armando
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 09.01.2023
Predmet:
Automatic programming
Branch-and-bound
Software and its engineering
Theory of computation
Library Learning
Abstraction Learning
Program Synthesis
ISSN:2475-1421, 2475-1421
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Popis
Shrnutí:This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping.
ISSN:2475-1421
2475-1421
DOI:10.1145/3571234