Model-Based Software and Systems Engineering

12th International Conference, MODELSWARD 2024, Rome, Italy, February 21-23, 2024, Revised Selected Papers
Herausgegeben:José Domínguez Mayo, Francisco; Ferreira Pires, Luís; Seidewitz, Edwin

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Communications in Computer and Information Science 2547
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-96840-2
• Seitenzahl: 259
• Erscheinungstermin: Juli 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9783031968402
• Artikelnr.: 74305151

Inhaltsangabe

.- 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.