Program boosting including using crowdsourcing for correctness
First Claim
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1. In a computing environment, a method performed by at least a processing unit, the method comprising;
- obtaining candidate programs related to a programming task;
synthesizing the candidate programs to produce blended programs;
providing positive examples and negative examples from the blended programs to crowd Sources;
obtaining a training set and feedback from the crowd sources;
determining fitness measures for the blended programs based on the training set and the feedback, the fitness measures including calculating an accuracy of the training set, the accuracy of the training set being based on a first symbolic finite automaton for the positive examples, a second finite automaton for the negative examples and a third symbolic finite automaton for the blended programs; and
selecting a most fit blended program from the blended programs based upon the fitness measures and using the selected most fit blended program for the programming task.
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Abstract
The subject disclosure is directed towards crowd-based approach to boosting the correctness of a computer program. Results from candidate programs obtained from a first crowd and which may be blended with one another into synthesized programs are sent to a second crowd for evaluation. Based upon the results, a training set evolves and programs are filtered based upon fitness. The process of blending and fitness evaluation with an evolved training set may be iteratively repeated to find a most fit program.
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Citations
20 Claims
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1. In a computing environment, a method performed by at least a processing unit, the method comprising;
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obtaining candidate programs related to a programming task; synthesizing the candidate programs to produce blended programs; providing positive examples and negative examples from the blended programs to crowd Sources; obtaining a training set and feedback from the crowd sources; determining fitness measures for the blended programs based on the training set and the feedback, the fitness measures including calculating an accuracy of the training set, the accuracy of the training set being based on a first symbolic finite automaton for the positive examples, a second finite automaton for the negative examples and a third symbolic finite automaton for the blended programs; and selecting a most fit blended program from the blended programs based upon the fitness measures and using the selected most fit blended program for the programming task. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. In a computing environment, a system comprising;
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memory configured with a program selection mechanism; at least one processor coupled to the memory to execute the program selection mechanism, and the program selection mechanism configured to; a) obtain candidate programs related to a programming task from a first crowd source; b) generate results from blended programs synthesized from the candidate programs; c) provide information corresponding to the results to a second crowd source; d) obtain training data and feedback from the second crowd source; e) process the feedback from the second crowd source into evaluation data; f) determine fitness data for the blended programs based at least in part on the evaluation data, wherein determining fitness data includes calculating an accuracy of the training data, wherein the accuracy of the training data is based on a first symbolic finite automaton for positive examples in the training data, a second finite automaton for negative examples in the training data, and a third symbolic finite automaton for the blended programs; and g) output a most fit blended program for the programming task from amongst the blended programs based upon the fitness data. - View Dependent Claims (15)
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16. One or more machine-readable storage media or hardware logic having machine-executable instructions, which when executed by one or more processors, perform operations comprising:
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a) blending a set of programs obtained from a first crowd source into synthesized programs, the set of programs relating to a programming task; b) generating results from the synthesized programs using training data in a current state; c) providing information corresponding to the results to a second crowd source; d) processing feedback from the second crowd source to evolve the training data into a new current state; e) determining fitness data for each of the synthesized programs, and associating each synthesized program with its fitness data, wherein determining the fitness data includes calculating an accuracy of the training data that is in the new current state, wherein the accuracy of the training data in the new current state is based on a first symbolic finite automaton for positive examples in the training data in the new current state, a second finite automaton for negative examples in the training data in the new current state, and a third symbolic finite automaton for the synthesized programs; f) filtering the synthesized programs based on each synthesized program'"'"'s associated fitness data to obtain a new set of programs; g) returning to operation a) until the fitness data associated with a program achieves a desired fitness level or another stopping criterion is met; and h) selecting a program based upon the fitness data associated with that program for the programming task. - View Dependent Claims (17, 18, 19, 20)
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Specification