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2016
ACM

Automatic patch generation by learning correct code

1 years 8 months ago
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 opensource software repositories to learn a probabilistic, applicationindependent 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. Categories and Subject Descriptors F.3.2 [Semantics of Programming Languages]: Program Analysis; D.2.5 [SOFTWARE ENGINEERING]: Testing and Debugging Keywords Program repair, Code correctness model, Learning correct code
Fan Long, Martin Rinard
Added 09 Apr 2016
Updated 09 Apr 2016
Type Journal
Year 2016
Where POPL
Authors Fan Long, Martin Rinard
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