fuddly - Fuzzing and Data Manipulation Framework | HACK4NET

List of features

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)
- scenario infrastructure (for modeling protocol logic)
- virtual operators
and so on...

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 package test include all unit & integration test cases of fuddly itself. Usage is as follows:

To launch all the tests, issue the command:
```
>> python -m test -a
```
To launch all the tests but the longer ones, issue the command:
```
>> python -m test
```
To avoid data model specific test cases use the option --ignore-dm-specifics

To launch a specific test category issue the following command:

>> python -m test test.<test_package>.<test_module>.<Test_Class>.<test_method>

Don't forget to populate ~/fuddly_data/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
- graphviz: For graphic visualization (e.g., scenario display)
- paramiko: Python implementation of the SSHv2 protocol
- serial: For serial port access
- cups: Python bindings for libcups
- rpyc: Remote Python Call (RPyC), a transparent and symmetric RPC library
For testing:
- ddt: Used for data-driven tests
- mock: Used for mocking (only needed in Python2)
For documentation generation: