Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing
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.
Keywords
Cite
@article{arxiv.1810.08289,
title = {Sample-Free Learning of Input Grammars for Comprehensive Software Fuzzing},
author = {Rahul Gopinath and Björn Mathis and Mathias Höschele and Alexander Kampmann and Andreas Zeller},
journal= {arXiv preprint arXiv:1810.08289},
year = {2018}
}