Input test generator using illumination search algorithm

Installing Python Binding to the Potrace library

$ sudo apt-get install build-essential python-dev libagg-dev libpotrace-dev pkg-config

Install pypotrace:

$ git clone https://github.com/flupke/pypotrace.git
$ cd pypotrace
$ pip install numpy
$ pip install .

Installing PyCairo and PyGObject

Instructions provided by https://pygobject.readthedocs.io/en/latest/getting_started.html#ubuntu-getting-started.

$ apt-get install python3-gi python3-gi-cairo gir1.2-gtk-3.0
$ apt-get install libgirepository1.0-dev gcc libcairo2-dev pkg-config python3-dev gir1.2-gtk-3.0 librsvg2-dev

Installing Other Dependencies

This tool has other dependencies such as tensorflow and deap.

To easily install the dependencies with pip:

$ pip install -r requirements.txt

Otherwise, you can manually install each required library listed in the requirements.txt file using pip.

• A trained model in h5 format. The default one is in the folder models;
• A list of seeds used for the input generation. In this implementation, the seeds are indexes of elements of the MNIST dataset. The default list is in the file bootstraps_five;
• properties.py containing the configuration of the tool selected by the user.

When the run is finished, the tool produces the following outputs in the logs folder:

• maps representing inputs distribution;
• json files containing the final reports of the run;
• folders containing the generated inputs (in image format).

Run the command: python mapelites_mnist.py

To build the docker image to be posted on DockerHub for running DeepHyperion we can run the following command from the project root (where the Dockerfile is located(:

docker build -t deephyperion:3.6 --build-arg SSH_PRIVATE_KEY="$(cat ~/.ssh/id_rsa)" .

Once this is build, the experiment can be executed using the following command:

docker run -v <YOUR_LOCAL_FOLDER>:/deephyperion/logs -it deephyperion:3.6

where -v <YOUR_LOCAL_FOLDER>:/deephyperion/logs will make sure that the output of DeepHyperion will be written to <YOUR_LOCAL_FOLDER>

To customize the experiment (not yet fully tested) you can specify properties on the command line. For example, to change the overall duration of the run you can add the following option to the docker run command:

--env DH_RUNTIME=30

Time is in seconds

To change the log interval instead you can add the following option to the docker run command:

--env DH_INTERVAL=10

Time is in seconds