Overview

This volume constitutes the revised selected papers of 12th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2024, in Rome, Italy, during February 21–23, 2024. The 7 full papers and 6 short papers included in this book were carefully reviewed and selected from 47 submissions. The papers are categorized under the topical sections as follows: Methodologies, Processes and Platforms; Modeling Languages, Tools and Architectures.

Full Product Details

Author:   Francisco José Domínguez Mayo ,  Luís Ferreira Pires ,  Edwin Seidewitz
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Volume:   2547
ISBN:  

9783031968402

ISBN 10:   3031968409
Pages:   270
Publication Date:   21 August 2025
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

.- Methodologies, Processes and Platforms. .- A Framework for Comparative Analysis of News Content: A Model-Based Approach. .- Analyzing Side-Tracking of Developers Using Object-Centric Process Mining. .- Enhancing Scenario-Based Modeling Using Large Language Models. .- Model-Driven Development of Chatbot Microservices. .- DynaTool: A Tool for Optimizing Hybrid Software Process. .- Modeling Languages, Tools and Architectures. .- Specifying, Analysing and Implementing Decision-Support System Architectures. .- An Approach for the Comparative Evaluation of RequirementsFormalisation Approaches. .- A Pluggable Type Checker for Representing Kinds of Quantities. .- Model-Driven Engineering for Data Provenance: A Graphical W3C PROV Modeling Tool. .- LLM as a Code Generator in Agile Model Driven Development. .- A Modeling Framework for Hardware-Software Systems with Machine Learning Components. .- Code Generation for Smart Contracts in Enterprise Application Integration. .- Deploying Machine Learning for Automatic Metamodel Instance Generation.

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