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Generating valid test inputs for a program is much easier if one knows theinput language. We present first successes for a technique that, given aprogram P without any input samples or models, learns an input grammar thatrepresents the syntactically valid inputs for P -- a grammar which can then beused for highly effective test generation for P . To this end, we introduce atest generator targeted at input parsers that systematically explores parsingalternatives based on dynamic tracking of constraints; the resulting inputs gointo a grammar learner producing a grammar that can then be used for fuzzing.In our evaluation on subjects such as JSON, URL, or Mathexpr, our PYGMALIONprototype took only a few minutes to infer grammars and generate thousands ofvalid high-quality inputs.

Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing