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- W2896517504 abstract "Generating valid test inputs for a program is much easier if one knows the input language. We present first successes for a technique that, given a program P without any input samples or models, learns an input grammar that represents the syntactically valid inputs for P -- a grammar which can then be used for highly effective test generation for P . To this end, we introduce a test generator targeted at input parsers that systematically explores parsing alternatives based on dynamic tracking of constraints; the resulting inputs go into 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 PYGMALION prototype took only a few minutes to infer grammars and generate thousands of valid high-quality inputs." @default.
- W2896517504 created "2018-10-26" @default.
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- W2896517504 date "2018-10-18" @default.
- W2896517504 modified "2023-09-27" @default.
- W2896517504 title "Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing" @default.
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