José Domínguez Mayo / Ferreira Pires / Seidewitz Model-Based Software and Systems Engineering
Erscheinungsjahr 2025
ISBN: 978-3-031-96841-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
12th International Conference, MODELSWARD 2024, Rome, Italy, February 21–23, 2024, Revised Selected Papers
E-Book, Englisch, 270 Seiten
Reihe: Computer Science (R0)
ISBN: 978-3-031-96841-9
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
.- 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.




