The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Matilda: A Generic and Tailorable Framework for Direct Model Execution in Model-Driven Software Development
Abstract
Traditional Model Driven Development (MDD) frameworks have three critical issues: (1) abstraction gap between modeling and programming layers, (2) a lack of traceability between models and programs, and (3) a lack of customizability to support various combinations of modeling technologies and implementation/deployment technologies. In order to address these issues, this chapter proposes a new MDD framework, called Matilda, which is a framework to build execution runtime engines (or virtual machines) for software models. It directly executes models defined with certain modeling technologies such as UML and BPMN by automatically transforming them to executable code. Matilda is designed based on the Pipes and Filters architectural pattern, which allows for configuring its structure and behavior flexibly by replacing one plugin with another one or changing the order of plugins. Also, plugins can be deployed on multiple network hosts and seamlessly connect them to form a pipeline. This facilitates distributed software development in which developers collaboratively work at physically dispersed places. This chapter overviews Matilda’s architectural design, describes the implementations of Matilda-based virtual machines, and evaluates their performance.
Related Content
|
Subhadip Kowar, Sneha Mukherjee, Shramana Ghosh.
© 2025.
26 pages.
|
|
C. V. Suresh Babu, Mala Raja Sekhar, A. Sachin, Bala Brindha.
© 2025.
26 pages.
|
|
A. D. N. Sarma.
© 2025.
32 pages.
|
|
Muhammad Usman Tariq.
© 2025.
26 pages.
|
|
Maaike Stoops, Pablo Alfonso Aguilar Calderón, Óscar Manuel Peña Bañuelos.
© 2025.
30 pages.
|
|
Pablo Alfonso Aguilar Calderón, José Alfonso Aguilar-Calderón, Dominik Morales-Silva, Carolina Tripp-Barba, Pedro Alfonso Aguilar-Calderón, Aníbal Zaldívar-Colado, Oscar Manuel Peña-Bañuelos.
© 2025.
30 pages.
|
|
Carlos Villarrubia, David Granada, Juan Manuel Vara.
© 2025.
34 pages.
|
|
|