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Model Checker Collection for the Model Checking Contest @ Petri nets

The model checker collection is a tool that is built to compete in the Model Checking Contest @ Petri nets using the tools that are already competing.

Its purpose is to allow research on the algorithm that will choose the best tool, depending on the model, examination and formula characteristics.

Installing the tool

This tool can be installed easily with pip from the sources:

$  git clone \
&& cd mcc4mcc \
&& pip install .

We currently do not distribute a packaged version.

Obtaining the tool submission kit and models

The MCC Submission Kit can be downloaded automatically, and models extracted from it using the following command:

$ ./prepare

The submission kit is put in the ToolSubmissionKit directory, and models are copied in the models directory.

Running the tool

Help can be obtained through:

$ python3 -m mcc4mcc \

Extracting information from the previous edition

The following command extracts known and learned data from the results of the 2017 edition of the Model Checking Contest:

$ python3 -m mcc4mcc \
    extract \

It creates several files, that are used to chose the correct tool to run:

  • <prefix>-configuration.json
  • <prefix>-known.json
  • <prefix>-learned.json
  • <prefix>-learned.<algorithm>.p
  • <prefix>-values.json

Running the model checker collection

The following command runs mcc4mcc with the state space examination on the model stored in ./models/TokenRing-PT-005.tgz. The prefix option tells the tool to use files generated with the given prefix. It allows users to generate files for several extraction parameters, and use them by giving their prefix.

$ python3 -m mcc4mcc \
    run \
    --examination=StateSpace \
    --input=./models/TokenRing-PT-005.tgz \

Testing the docker images

The following command tests if docker images can be run on some examinations and models of the Model Checking Contest:

$ python3 -m mcc4mcc \
    test \

Forgetting some model characteristics

In order to create more collisions between models given a set of characteristics, it can be interesting to forget some characteristics during machine learning. The --forget option allows us to do it, for instance:

$ python3 -m mcc4mcc extract \
    --duplicates \
    --year=2017 \
    --forget="Deadlock,Live,Quasi Live,Reversible,Safe"

Dropping some models during machine learning

It is interesting to remove some models during machine learning, in order to check that the algorithm is still able to obtain a good score. The --training option allows us to do it.

$ python3 -m mcc4mcc extract \
    --duplicates \
    --year=2017 \

The command above keeps only 25% of the models for learning, but still computes the score using all the models. The --duplicates option allows the tool to keep duplicate lines during machine learning.

Building the docker images

This repository provides scripts to build automatically the docker images using virtual machines of the previous edition of the Model Checking Contest. This is not optimal: tool developers should provide their own docker images for both simplicity and efficiency.

The following command builds all images and uploads them to the docker image repository mccpetrinets:

$ ./build

Building the virtual machine

The following command creates a virtual machine containing mcc4mcc dedicated to the Model Checking Contest:

$ ./create-vm

The virtual machine is created as mcc4mcc.vmdk.


Model Checker Collection for the Model Checking Contest @ Petri nets





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