ModelXGlue: a benchmarking framework for ML tools in MDE.
Gespeichert in:
| Titel: | ModelXGlue: a benchmarking framework for ML tools in MDE. |
|---|---|
| Autoren: | López, José Antonio Hernández, Cuadrado, Jesús Sánchez, Rubei, Riccardo, Di Ruscio, Davide |
| Quelle: | Software & Systems Modeling; Aug2025, Vol. 24 Issue 4, p1035-1058, 24p |
| Schlagwörter: | MACHINE learning, MODEL-driven software architecture, AUTOMATION software, BENCHMARKING (Management) |
| Abstract: | The integration of machine learning (ML) into model-driven engineering (MDE) holds the potential to enhance the efficiency of modelers and elevate the quality of modeling tools. However, a consensus is yet to be reached on which MDE tasks can derive substantial benefits from ML and how progress in these tasks should be measured. This paper introduces ModelXGlue , a dedicated benchmarking framework to empower researchers when constructing benchmarks for evaluating the application of ML to address MDE tasks. A benchmark is built by referencing datasets and ML models provided by other researchers, and by selecting an evaluation strategy and a set of metrics. ModelXGlue is designed with automation in mind and each component operates in an isolated execution environment (via Docker containers or Python environments), which allows the execution of approaches implemented with diverse technologies like Java, Python, R, etc. We used ModelXGlue to build reference benchmarks for three distinct MDE tasks: model classification, clustering, and feature name recommendation. To build the benchmarks we integrated existing third-party approaches in ModelXGlue. This shows that ModelXGlue is able to accommodate heterogeneous ML models, MDE tasks and different technological requirements. Moreover, we have obtained, for the first time, comparable results for these tasks. Altogether, it emerges that ModelXGlue is a valuable tool for advancing the understanding and evaluation of ML tools within the context of MDE. [ABSTRACT FROM AUTHOR] |
| Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Datenbank: | Complementary Index |
Full Text Finder 
Nájsť tento článok vo Web of Science Schreiben Sie den ersten Kommentar!