swMATH ID: 17448
Software Authors: Long, Fan; Rinard, Martin
Description: Automatic patch generation by learning correct code. We present Prophet, a novel patch generation system that works with a set of successful human patches obtained from open-source software repositories to learn a probabilistic, application-independent model of correct code. It generates a space of candidate patches, uses the model to rank the candidate patches in order of likely correctness, and validates the ranked patches against a suite of test cases to find correct patches. Experimental results show that, on a benchmark set of 69 real-world defects drawn from eight open-source projects, Prophet significantly outperforms the previous state-of-the-art patch generation system.
Homepage: http://dl.acm.org/citation.cfm?doid=2837614.2837617
Keywords: code correctness model; learning correct code; program repair
Related Software: SemFix; Angelix; GZoltar; Nopol; ASTOR; GenProg; DLFix; DeepFix; QuixBugs; Codeflaws; Qlose; JFIX; Defects4J; jGenProg; ARJA; jMetal; SQLizer; RobustFill; aplore3; Python
Cited in: 4 Documents

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