  Mutation testing has been widely accepted as an approach to guide test case
generation or to assess the effectiveness of test suites. Empirical studies
have shown that mutants are representative of real faults; yet they also
indicated a clear need for better, possibly customized, mutation operators and
strategies. While methods to devise domain-specific or general-purpose mutation
operators from real faults exist, they are effort- and error-prone, and do not
help the tester to decide whether and how to mutate a given source code
element. We propose a novel approach to automatically learn mutants from faults
in real programs. First, our approach processes bug fixing changes using
fine-grained differencing, code abstraction, and change clustering. Then, it
learns mutation models using a deep learning strategy. We have trained and
evaluated our technique on a set of ~787k bug fixes mined from GitHub. Our
empirical evaluation showed that our models are able to predict mutants that
resemble the actual fixed bugs in between 9% and 45% of the cases, and over 98%
of the automatically generated mutants are lexically and syntactically correct.
