Using the ModelSet dataset to support Machine Learning in Model-Driven Engineering
The availability of curated collections of models is essential for the application of techniques like Machine Learning (ML) and Data Analytics to MDE as well as to boost research activities.
However, many applications of ML to address MDE tasks are currently limited to small datasets. In this demo we will present ModelSet, a dataset composed of 5,000 Ecore models and 5,000 UML models which has been manually labelled to support Machine Learning tasks (http://modelset.github.io). ModelSet is built upon the models collected by the MAR search engine (http://mar-search.org), which provides more than 500,000 models of different types. We will describe the structure of the dataset and we will explain how to use the associated library to develop ML applications in Python. Finally, we will describe some applications which can be addressed using ModelSet.
Wed 26 OctDisplayed time zone: Eastern Time (US & Canada) change
15:30 - 17:00
|Machine learning methods for model classification: A comparative studyFT
José Antonio Hernández López Universidad de Murcia, Riccardo Rubei University of L'Aquila, Jesús Sánchez Cuadrado Universidad de Murcia, Davide Di Ruscio University of L'AquilaPre-print
|Enhancing software model encoding for feature location approaches based on machine learning techniquesJ1st
Ana Cristina Marcén , Francisca Pérez SVIT Research Group. Universidad San Jorge, Oscar Pastor Universitat Politecnica de Valencia, Carlos Cetina San Jorge University, SpainLink to publication
|ModelSet: a dataset for machine learning in model-driven engineeringJ1st
José Antonio Hernández López Universidad de Murcia, Javier Luis Cánovas Izquierdo IN3 - UOC, Jesús Sánchez Cuadrado Universidad de MurciaLink to publication
|Using the ModelSet dataset to support Machine Learning in Model-Driven EngineeringDemo
Tools & Demonstrations