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Random program generation — fuzzing — is an effective technique for discovering bugs in compilers but successful fuzzers require extensive development effort for every language supported by the compiler, and often leave parts of the language space untested.

We introduce DeepSmith, a novel machine learning approach to accelerating compiler validation through the inference of generative models for compiler inputs. Our approach infers a learned model of the structure of real world code based on a large corpus of open source code. Then, it uses the model to automatically generate tens of thousands of realistic programs. Finally, we apply established differential testing methodologies on them to expose bugs in compilers. We apply our approach to the OpenCL programming language, automatically exposing bugs with little effort on our side. In 1,000 hours of automated testing of commercial and open source …