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Generating Component-based Supervised Learning Programs From Crowdsourced Examples
(2017-12-21)
We present CrowdLearn, a new system that processes an existing corpus of crowdsourced machine learning programs to learn how to generate effective pipelines for solving supervised machine learning problems. CrowdLearn uses ...
Using Active Learning to Synthesize Models of Applications That Access Databases
(2018-08-28)
We present a new technique that uses active learning to infer models of
applications that manipulate relational databases. This technique
comprises a domain-specific language for modeling applications that
access ...
Inference and Regeneration of Programs that Store and Retrieve Data
(2017-04-24)
As modern computation platforms become increasingly complex, their programming interfaces are increasingly difficult to use. This complexity is especially inappropriate given the relatively simple core functionality that ...
Automatic Inference of Code Transforms and Search Spaces for Automatic Patch Generation Systems
(2016-07-08)
We present a new system, Genesis, that processes sets of human patches to automatically infer code transforms and search spaces for automatic patch generation. We present results that characterize the effectiveness of the ...
Efficient Specification-Assisted Error Localization and Correction
(2003-11-13)
We present a new error localization tool, Archie, that accepts aspecification of key data structure consistency constraints, then generatesan algorithm that checks if the data structures satisfy theconstraints. We also ...
Energy-Efficient Approximate Computation in Topaz
(2014-08-19)
We present Topaz, a new task-based language for computations that execute on approximate computing platforms that may occasionally produce arbitrarily inaccurate results. The Topaz implementation maps approximate tasks ...
Prophet: Automatic Patch Generation via Learning from Successful Patches
(2015-07-13)
We present Prophet, a novel patch generation system that learns a probabilistic model over candidate patches from a database of past successful patches. Prophet defines the probabilistic model as the combination of a ...
Automatic Discovery and Patching of Buffer and Integer Overflow Errors
(2015-05-26)
We present Targeted Automatic Patching (TAP), an automatic buffer and integer overflow discovery and patching system. Starting with an application and a seed input that the application processes correctly, TAP dynamically ...
Prophet: Automatic Patch Generation via Learning from Successful Human Patches
(2015-05-26)
We 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 ...
Automatic Input Rectification
(MIT CSAIL, 2011-10-03)
We present a novel technique, automatic input rectification, and a prototype implementation called SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process ...