ModelSet: a dataset for machine learning in model-driven engineering

Abstract

The application of machine learning (ML) algorithms to address problems related to model-driven engineering (MDE) is currently hindered by the lack of curated datasets of software models. There are several reasons for this, including the lack of large collections of good quality models, the difficulty to label models due to the required domain expertise, and the relative immaturity of the application of ML to MDE. In this work, we present ModelSet, a labelled dataset of software models intended to enable the application of ML to address software modelling problems. To create it we have devised a method designed to facilitate the exploration and labelling of model datasets by interactively grouping similar models using off-the-shelf technologies like a search engine. We have built an Eclipse plug-in to support the labelling process, which we have used to label 5,466 Ecore meta-models and 5,120 UML models with its category as the main label plus additional secondary labels of interest. We have evaluated the ability of our labelling method to create meaningful groups of models in order to speed up the process, improving the effectiveness of classical clustering methods. We showcase the usefulness of the dataset by applying it in a real scenario: enhancing the MAR search engine. We use ModelSet to train models able to infer useful metadata to navigate search results. The dataset and the tooling are available at https://figshare.com/s/5a6c02fa8ed20782935c and a live version at http://modelset.github.io.

ModelXGlue: a benchmarking framework for ML tools in MDE

“…The ModelSet dataset. 8 López et al [29] considered a subset of the Ecore and UML models collected by the MAR search engine [30,31] and labeled them. As a result, ModelSet was released in 2021.…”

“…This task has proved useful to facilitate the navigation of large model repositories by users. In particular, it has been used to implement faceted search in the MAR search engine [29].…”

“…Furthermore, the source code released in those works is not in a form that is easily runnable. The dataset presented in[28] is accompanied by two exemplary applications of model clas-…”

Towards using Few-Shot Prompt Learning for Automating Model Completion

“…We followed a manual data sampling methodology, which consisted of selecting 30 domain models from a larger dataset called ModelSet [10]. The goal was to have a dataset with samples of different sizes and containing domain models covering multi-disciplinary domains such as education, finance, entertainment, etc.…”

Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

“…A dataset is basically a set of information that is provided to train the ML tool for predicting future events [ 52 ]. It forms the foundation for training and analyzing ML models.…”