Please note that this version of VerveineJ is compatible with Moose-10 (and possibly later). It is based on a meta-model that does not match the one of previous versions of Moose, hence, they are not able to load the models generated by this VerveineJ.
A Java to JSON/MSE importer.
Based on JDT, it parses java code to export it in the MSE or JSON formats used by the Moose data analysis platform.
There is a docker version.
It will create an output.json
(JSON format also available) file with the model extracted from the <java-source-directory>
There is a docker version at: https://github.com/Evref-BL/VerveineJ-Docker) (thanks to Benoît Verhaeghe for this).
If you already have docker the summary is:
docker run -v <full/path/toSource>:/src [-v <full/path/toDependency>:/dependency] ghcr.io/evref-bl/verveinej:latest <verveineJOption>
The resulting model file (.json or .mse) is in <full/path/toSource>.
Otherwise, on the command line, download the jar of the latest release and the command is
java -jar VerveineJ-v1.0.0.jar -format json -autocp <dependency/folder> <src/folder>
It will create an output.json
file with the model extracted from the <java-source-directory>
To see what other options are available:
java -jar VerveineJ-v1.0.0.jar -h
We do use gradle to compile and load dependencies.
Since we load project from github repository.
Please create a file named gradle.properties
in the root of this project as follow
gpr.user=<USERNAME>
gpr.token=<TOKEN>
we expect here your <username> and <token> from GitHub platform. Please never commit them
It is possible to run the tests and generate a jacoco test report with
./gradlew app:jacocoTestReport
You can build the project and produce a fat jar with
./gradlew app:shadowJar
Sometimes, because of the JVM optimization, stacktraces are not fully displayed. To force JVM keep the full stacktrace, you can use the following vm options:
-XX:-OmitStackTraceInFastThrow -Xint
This will make the execution super slow, so keep this option for debug purpose only.