Efficient Replay-based Regression Testing for Distributed Reactive Systems in the Context of Model-driven Development

2021 ◽  
Author(s):  
Majid Babaei ◽  
Juergen Dingel
Keyword(s):  
Reactive Systems ◽  
Regression Testing ◽  
Model Driven Development ◽  
Model Driven

FORMAL MODELS IN INDUSTRY STANDARD TOOLS: AN ARGOS BLOCK WITHIN SIMULINK

2005 ◽  
Vol 15 (02) ◽  
pp. 389-395 ◽  
Author(s):  
T. BOURKE ◽  
A. SOWMYA
Keyword(s):  
Reactive Systems ◽  
Formal Models ◽  
Synchronous Languages ◽  
Model Driven Development ◽  
Graphical Notation ◽  
Model Driven ◽  
Industry Standard

Simulink is widely used within the industry for simulation and model-driven development, and reactive behaviors are often modeled using an add-on called Stateflow. Argos is one of the synchronous languages that have been proposed for the specification, validation and implementation of reactive systems. It is a rigorously defined graphical notation which, though not as powerful as Stateflow, is much less complicated. This paper describes the implementation of an Argos block for Simulink.

Model-driven development of composite context-aware web applications

2009 ◽  
Vol 51 (8) ◽  
pp. 1244-1260 ◽  
Author(s):  
Georgia M. Kapitsaki ◽  
Dimitrios A. Kateros ◽  
George N. Prezerakos ◽  
Iakovos S. Venieris
Keyword(s):  
Web Applications ◽  
Context Aware ◽  
Model Driven Development ◽  
Model Driven

scholarly journals Model-driven development platform selection: four industry case studies

2021 ◽  
Author(s):  
Siamak Farshidi ◽  
Slinger Jansen ◽  
Sven Fortuin
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Decision Makers ◽  
Development Activity ◽  
Model Driven Development ◽  
Domain Experts ◽  
Theoretical Contribution ◽  
Model Driven ◽  
Development Platform ◽  
Software Production

AbstractModel-driven development platforms shift the focus of software development activity from coding to modeling for enterprises. A significant number of such platforms are available in the market. Selecting the best fitting platform is challenging, as domain experts are not typically model-driven deployment platform experts and have limited time for acquiring the needed knowledge. We model the problem as a multi-criteria decision-making problem and capture knowledge systematically about the features and qualities of 30 alternative platforms. Through four industry case studies, we confirm that the model supports decision-makers with the selection problem by reducing the time and cost of the decision-making process and by providing a richer list of options than the enterprises considered initially. We show that having decision knowledge readily available supports decision-makers in making more rational, efficient, and effective decisions. The study’s theoretical contribution is the observation that the decision framework provides a reliable approach for creating decision models in software production.

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