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dc.contributor.advisorMartin Rinard
dc.contributor.authorLong, Fanen_US
dc.contributor.authorRinard, Martinen_US
dc.contributor.otherProgram Analysisen
dc.date.accessioned2015-05-26T22:00:02Z
dc.date.available2015-05-26T22:00:02Z
dc.date.issued2015-05-26
dc.identifier.urihttp://hdl.handle.net/1721.1/97088
dc.description.abstractWe present Prophet, a novel patch generation system that learns a probabilistic model over candidate patches from a large code database that contains many past successful human patches. It defines the probabilistic model as the combination of a distribution over program points based on error localization algorithms and a parameterized log-linear distribution over modification operations. It then learns the model parameters via maximum log-likelihood, which identifies important characteristics of the successful human patches. For a new defect, Prophet generates a search space that contains many candidate patches, applies the learned model to prioritize those potentially correct patches that are consistent with the identified successful patch characteristics, and then validates the candidate patches with a user supplied test suite.en_US
dc.format.extent7 p.en_US
dc.relation.ispartofseriesMIT-CSAIL-TR-2015-019
dc.titleProphet: Automatic Patch Generation via Learning from Successful Human Patchesen_US
dc.date.updated2015-05-26T22:00:02Z


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