Just-in-time learning for bottom-up enumerative synthesis

A key challenge in program synthesis is the astronomical size of the search space the synthesizer has to explore. In response to this challenge, recent work proposed to guide synthesis using learned probabilistic models. Obtaining such a model, however, might be infeasible for a problem domain where...

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Veröffentlicht in:	Proceedings of ACM on programming languages Jg. 4; H. OOPSLA; S. 1 - 29
Hauptverfasser:	Barke, Shraddha, Peleg, Hila, Polikarpova, Nadia
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 13.11.2020
Schlagworte:	Context specific languages Domain specific languages Programming by example Software and its engineering Software notations and tools Probabilistic models Domain-specific languages Program Synthesis
ISSN:	2475-1421, 2475-1421
Online-Zugang:	Volltext
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