abstract.tex

\begin{abstract}
  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 \emph{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 compilers, we
  discover bugs in all of them, submitting 67 bug reports. Our test cases are on
  average two orders of magnitude smaller than the state-of-the-art, require
  $3.03\times$ less time to generate and evaluate, and expose bugs which the
  state-of-the-art cannot. Our random program generator, comprising only 500
  lines of code, took 12 hours to train for OpenCL versus the state-of-the-art
  taking 9 man months to port from a generator for C and 50,000 lines of code.
  With 18 lines of code we extended our program generator to a second language,
  uncovering crashes in Solidity compilers in 12 hours of automated testing.
\end{abstract}