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dc.contributor.authorBragg, Nate FF
dc.contributor.authorFoster, Jeffrey S
dc.contributor.authorRoux, Cody
dc.contributor.authorSolar-Lezama, Armando
dc.date.accessioned2022-07-20T15:25:29Z
dc.date.available2022-07-20T15:25:29Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/143886
dc.description.abstractSketch is a popular program synthesis tool that solves for unknowns in a sketch or partial program. However, while Sketch is powerful, it does not directly support modular synthesis of dependencies, potentially limiting scalability. In this paper, we introduce Sketcham, a new technique that modularizes a regular sketch by automatically generating mocks—functions that approximate the behavior of complete implementations—from the sketch’s test suite. For example, if the function f originally calls g, Sketcham creates a mock gm from g’s tests and augments the sketch with a version of f that calls gm. This change allows the unknowns in f and g to be solved separately, enabling modular synthesis with no extra work from the Sketch user. We evaluated Sketcham on ten benchmarks, performing enough runs to show at a 95% confidence level that Sketcham improves median synthesis performance on six of our ten benchmarks by a factor of up to 5× compared to plain Sketch, including one benchmark that times out on Sketch, while exhibiting similar performance on the remaining four. Our results show that Sketcham can achieve modular synthesis by automatically generating mocks from tests.en_US
dc.language.isoen
dc.publisherSpringer International Publishingen_US
dc.relation.isversionof10.1007/978-3-030-81685-8_38en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceSpringeren_US
dc.titleProgram Sketching by Automatically Generating Mocks from Testsen_US
dc.typeArticleen_US
dc.identifier.citationBragg, Nate FF, Foster, Jeffrey S, Roux, Cody and Solar-Lezama, Armando. 2021. "Program Sketching by Automatically Generating Mocks from Tests." COMPUTER AIDED VERIFICATION (CAV 2021), PT I, 12759.
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalCOMPUTER AIDED VERIFICATION (CAV 2021), PT Ien_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-07-20T15:21:34Z
dspace.orderedauthorsBragg, NFF; Foster, JS; Roux, C; Solar-Lezama, Aen_US
dspace.date.submission2022-07-20T15:21:35Z
mit.journal.volume12759en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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