This is the replication package for the paper "NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering" where we present Neo Modeling Framework (NeoMF), an open-source set of tools primarily designed to manipulate ultra-large datasets in the Neo4j database.  

Repository structure

• NeoModelingFramework.zip - contains the replication package, including the source code for NeoMF, test files to run the evaluation, used artifacts, and instructions to run the framework. The most import folders are listed below:
  • codeGenerator - NeoMF generator module
  • modelLoader - NeoMF loader module
  • modelEditor - NeoMF core module
  • Evaluation - contains the evaluation artifacts and results (a copy
    • dataset- Files used for the evaluation, including metamodels (.ecore) and models (.xmi)
    • results - CSV files with the results from RQ1, RQ2 and RQ3
    • analysis - Jupyter notebooks used to analyze and plot the results

Running NeoMF

The best way to run NeoMF is following the instructions at our GitHub repository. A copy of the Readme file is also present inside the zip file available here.

Empirical Evaluation

Make sure that you follow the instructions to run NeoMF.

The quantitative evaluation can be re-run by running RQ1Eval.kt, RQ2Eval.kt, RQ2EvalModels.kt, RQ2EvalJava.kt inside modelLoader/src/test/kotlin/evaluation and RQ3Eval.kt, RQ3EvalModels.kt, RQ3EvalJava.kt inside modelEditor/src/test/kotlin/evaluation.

Make sure that you have an empty instance of Neo4j running. Results will be generated as CSV files, under Evaluation/results and the results can be plotted by running the Jupyter Notebooks at Evaluation/analysis.

Please note that due to differences in hardware, re-running the experiments will probably generate slightly different results than those reported in the paper.