• Graph-based data model that enables:
  • to represent complex data formats and also to mix them
  • complex data manipulations
  • to dissect/absorb existing data
  • generation & mutation fuzzing strategy
• Fuzzing automation framework:
  • target abstraction
  • monitoring means based on independant probes
  • replay & logging
  • data manipulation based on disruptors (objects that implement specific data transformation)
  • virtual operator abstraction
• and so on...

• Full documentation
• Refer to TODO file

• Documentation is available here.
• In order to generate the documentation from the source, follow these steps:
  1. go to the folder docs/
  2. execute make html to generate HTML documentation
  3. execute make latexpdf to generate PDF documentation
  4. generated documentation is located in docs/build/

The file fuzzfmk/test.py include all unit & integration test cases of fuddly itself. Usage is as follows:

• To launch all the test, issue the command:

  >> python fuzzfmk/test.py -a

• To launch all the test but the longer ones, issue the command:

  >> python fuzzfmk/test.py

• To avoid data model specific test cases use the option --ignore-dm-specifics

• To launch a specific test category issue the folowing command:

  >> python fuzzfmk/test.py <Test_Class>.<test_method>

• Don't forget to populate imported_data/ with sample files for data models that need it

• Compatible with Python2 and Python3
• Mandatory:
  • six: Python 2/3 compatibility
  • sqlite3: SQLite3 data base
• Optional:
  • xtermcolor: Terminal color support
  • cups: Python bindings for libcups
  • rpyc: Remote Python Call (RPyC), a transparent and symmetric RPC library
• For documentation generation:
  • sphinx: sphinx >= 1.3 (with builtin napoleon extension)
  • texlive (optional): Needed to generate PDF documentation
  • readthedocs theme (optional): Privileged html theme for sphinx