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Model-Based Software and Systems Engineering: 12th International Conference, MODELSWARD 2024, Rome, Italy, February 2123, 2024, Revised Selected Papers [Minkštas viršelis]

  • Formatas: Paperback / softback, 259 pages, aukštis x plotis: 235x155 mm, 113 Illustrations, black and white; X, 259 p. 113 illus., 1 Paperback / softback
  • Serija: Communications in Computer and Information Science 2547
  • Išleidimo metai: 28-Jul-2025
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031968409
  • ISBN-13: 9783031968402
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 259 pages, aukštis x plotis: 235x155 mm, 113 Illustrations, black and white; X, 259 p. 113 illus., 1 Paperback / softback
  • Serija: Communications in Computer and Information Science 2547
  • Išleidimo metai: 28-Jul-2025
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031968409
  • ISBN-13: 9783031968402
Kitos knygos pagal šią temą:

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.

.- 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.