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 DomainSpecific API used for the Train benchmark scenario is available at modelEditor\src\main\kotlin\geodes\sms\nmf\editor\railway and the generic api code is located at modelLoader/src/test/kotlin/evaluation 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.