NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering (replication package)

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 (NMF), 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 NMF, test files to run the evaluation, used artifacts, and instructions to run the framework. The most import folders are listed below: codeGenerator - NMF generator module modelLoader - NMF loader module modelEditor - NMF editor module Evaluation - contains the evaluation artifacts and results (a copy models - Ecore files used for RQ1 results - CSV files with the results from RQ1 and RQ2 analysis - Jupyter notebooks used to analyze and plot the results Running NMF The best way to run NMF 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 NMF. The quantitative evaluation can be re-run by running RQ1Eval.kt inside modelLoader/src/test/kotlin/evaluation and RQ2Eval.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.