Model-Driven Engineering and Software Development〈1st ed. 2023〉 : 9th International Conference, MODELSWARD 2021, Virtual Event, February 8–10, 2021, and 10th International Conference, MODELSWARD 2022, Virtual Event, February 6–8, 2022, Revised Selected Papers

著者名：Pires, Luís Ferreira (EDT)/Hammoudi, Slimane (EDT)/Seidewitz, Edwin (EDT)

Springer（2023/08/03発売）

• 言語：ENG
• ISBN：9783031388200
• eISBN：9783031388217

Description

This book constitutes the refereed post-proceedings of the 9th International Conference and 10th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2021 and MODELSWARD 2022, was held virtually due to the COVID-19 crisis on February 8–10, 2021 and February 6–8, 2022.

The 11 full papers included in this book were carefully reviewed and selected from 121 submissions. The purpose of the International Conference on model-driven engineering and software development is to provide a platform for researchers, engineers, academics as well as industrial professionals from all over the world to present their research results and development activities in using models and model driven engineering techniques for system development.

Table of Contents

A Digital Twin Description Framework and its Mapping to Asset Administration Shell.- Acknowledging Implementation Trade-offs when Developing with Units of Measurement.- HERO vs Zombie: Destroying Zombie Guests in Virtual Machine Environments.- Multi-view FMEA Re-Validation: Efficient Risk and Engineering Knowledge Integration in Agile Production Systems Engineering.- PSCS4CPP: A Generative PSCS Implementation for C++.- Dependency Graphs to Boost the Verification of SysML Models.- Decomposable and Executable Models for Verification of Real-Time Systems.- Comparing Goal-oriented Analysis Techniques: A Controlled Experiment.- A Methodological Framework for SPL Engineering from DSML.- W-Sec: a Model-based Formal Method for Assessing the Impacts of Security Countermeasures.- Managing Schema Migration in NoSQL Databases: Advisor Heuristics vs. Self-adaptive Schema Migration Strategies.