Guiding dynamic programing via structural probability for accelerating programming by example

Programming by example (PBE) is an important subproblem of program synthesis, and PBE techniques have been applied to many domains. Though many techniques for accelerating PBE systems have been explored, the scalability remains one of the main challenges: There is still a gap between the performance...

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Bibliographic Details
Published in:Proceedings of ACM on programming languages Vol. 4; no. OOPSLA; pp. 1 - 29
Main Authors: Ji, Ruyi, Sun, Yican, Xiong, Yingfei, Hu, Zhenjiang
Format: Journal Article
Language:English
Published: New York, NY, USA ACM 13.11.2020
Subjects:
General programming languages
Software and its engineering
Software notations and tools
Programming by Example
Dynamic Programming
Probabilistic Model
ISSN:2475-1421, 2475-1421
Online Access:Get full text
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Summary:Programming by example (PBE) is an important subproblem of program synthesis, and PBE techniques have been applied to many domains. Though many techniques for accelerating PBE systems have been explored, the scalability remains one of the main challenges: There is still a gap between the performances of state-of-the-art synthesizers and the industrial requirement. To further speed up solving PBE tasks, in this paper, we propose a novel PBE framework MaxFlash. MaxFlash uses a model based on structural probability, named topdown prediction models, to guide a search based on dynamic programming, such that the search will focus on subproblems that form probable programs, and avoid improbable programs. Our evaluation shows that MaxFlash achieves × 4.107− × 2080 speed-ups against state-of-the-art solvers on 244 real-world tasks.
ISSN:2475-1421
2475-1421
DOI:10.1145/3428292