Model Mime Experiments

M2 (Model Mime) is a generator that, given a dataset of models (that conform a meta-model) and a set of addition edit operations, generates models that are similar to the dataset under consideration.

Before running the experiments 🛠️

Prerequisites:

• Java 11
• Python 3.X
• Graphviz
• Maven

Install requirements.txt:

$ pip install -r requirements.txt

Compile maven:

$ cd java/model2graph
$ mvn package

Download EMF Random Instantiator:

• Download this jar.
• Put it in this folder.

Execute tests:

$ python -m unittest discover

Organization of the repository 📌

The repository contains the following folders and files:

• Folder baselines - It contains the synthetic models for each dataset and baseline generator (VIATRA, RANDOM and RandomEMF).
• Folder data - It contains the datasets, meta-models, and data used for testing.
• Folder dmg - All the Python code.
• Folder java - All the Java code.
• Folder models - Neural networks used to obtain the results presented in the paper.
• Folder scripts - Some useful scripts.
• Folder stats - It contains statistics used to assess the scalability for each dataset and baseline generator (VIATRA, RANDOM and RandomEMF)..
• Folder tests - It contains the test cases associated to the Python code.
• Folder vsconfigs - It contains the VIATRA config files.
• File requirements.txt - Python libraries.

Procedure 🚀

Steps 1 and 2 are already executed. To obtain the results of the paper, go directly to Step 3. If you want to generate models using the trained generators to see some examples, go to Step 4.

Step 1: Dataset generation

First of all, we have to preprocess all the datasets and split them into train and test sets. This is done by the following script:

$ ./scripts/generateDatasets.sh

We consider four datasets:

• yakindu-github: Dataset of Yakindu Statecharts crawled from GitHub. It was used in this paper.
• yakindu-exercise: Dataset of Yakindu Statecharts used in this paper.
• rds-genmymodel: Dataset of database models crawled from GenMyModel. It was used in this paper.
• ecore-github: Dataset of ecore models crawled from GithHub. It was used in this paper.

Step 2: Fitting and generation process

Case M2

To train the neural networks for each one of the datasets execute the following script:

$ ./scripts/trainModels.sh 

Case VIATRA generator

To generate the vscondigs for VIATRA, execute:

$ ./scripts/fitViatra.sh 

These config files will be the input of the VIATRA generator.

Case RandomEMF

The estimation of the parameters are in the notebooks of this folder.

Case EMF Random Instantiator

To generate models from EMF Random Instantiator, just execute:

scripts/fitRandomInstantiator.sh

Step 3: Assessment 📋

Once M2 is trained and the generated models of the other generators are in the right folder, we can compare the four generators by executing the following (consistency, diversity and realism):

scripts/assessmentScript.sh 

To evaluate the scalability just execute the following:

scripts/quantileScript.sh

Step 4: Model generation using M2 ⚙️

To generate models using the trained generator, just execute:

python scripts/generateModels.py -d rds-genmymodel -hi 64 -ms 300 -ps generatedModels/ -nm 100
python scripts/generateModels.py -d yakindu-github -hi 64 -ms 50 -ps generatedModels/ -nm 100
python scripts/generateModels.py -d yakindu-exercise -hi 64 -ms 150 -ps generatedModels/ -nm 100
python scripts/generateModels.py -d ecore-github -hi 64 -ms 200 -ps generatedModels/ -nm 100

where:

• -d establishes the dataset.
• -hi establishes the hidden dimension of the model (our model uses a hidden dim of 64).
• -ms establishes the maximum size of the generated models.
• -ps establishes the folder where the models will be saved.
• -nm establishes the number of models that will be generated.

If you want to use M2 in new use cases, please go to this repository. There M2 is coded as a Python library.