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Title: Efficient Bottom-Up Synthesis for Programs with Local Variables

We propose a new synthesis algorithm that can efficiently search programs with local variables (e.g., those introduced by lambdas). Prior bottom-up synthesis algorithms are not able to evaluate programs with free local variables, and therefore cannot effectively reduce the search space of such programs (e.g., using standard observational equivalence reduction techniques), making synthesis slow. Our algorithm can reduce the space of programs with local variables. The key idea, dubbed lifted interpretation, is to lift up the program interpretation process, from evaluating one program at a time to simultaneously evaluating all programs from a grammar. Lifted interpretation provides a mechanism to systematically enumerate all binding contexts for local variables, thereby enabling us to evaluate and reduce the space of programs with local variables. Our ideas are instantiated in the domain of web automation. The resulting tool, Arborist, can automate a significantly broader range of challenging tasks more efficiently than state-of-the-art techniques including WebRobot and Helena.

 
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Award ID(s):
2236233 2123654
PAR ID:
10488386
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Association for Computing Machinery
Date Published:
Journal Name:
Proceedings of the ACM on Programming Languages
Volume:
8
Issue:
POPL
ISSN:
2475-1421
Page Range / eLocation ID:
1540 to 1568
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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