Inductive program synthesis over noisy data
Name
3368089.3409732.pdf
Description
Published version
Size
835.34 KB
Format
Adobe PDF
Checksum (MD5)
ae046abd9ea179f135b16ba69ddc2274
Author(s)
Handa, S
Rinard, MC
Date Issued
2020
Journal
ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Publisher
ACM
Citation
Handa, S and Rinard, MC. 2020. "Inductive program synthesis over noisy data." ESEC/FSE 2020 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering.
Version
Final published version
Abstract
© 2020 Owner/Author. We present a new framework and associated synthesis algorithms for program synthesis over noisy data, i.e., data that may contain incorrect/corrupted input-output examples. This framework is based on an extension of finite tree automata called state-weighted finite tree automata. We show how to apply this framework to formulate and solve a variety of program synthesis problems over noisy data. Results from our implemented system running on problems from the SyGuS 2018 benchmark suite highlight its ability to successfully synthesize programs in the face of noisy data sets, including the ability to synthesize a correct program even when every input-output example in the data set is corrupted.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3368089.3409732
