Allowing end-users to participate within model-driven development approaches

Author(s):  
F. Perez ◽  
P. Valderas ◽  
J. Fons
2019 ◽  
Vol 9 (21) ◽  
pp. 4553 ◽  
Author(s):  
Tomaž Kos ◽  
Marjan Mernik ◽  
Tomaž Kosar

End-user programming may utilize Domain-Specific Modeling Languages (DSMLs) to develop applications in the form of models, using only abstractions found in a specific problem domain. Indeed, the productivity benefits reported from Model-Driven Development (MDD) are hard to ignore, and a number of MDD solutions are flourishing. However, not all stories from industry on MDD are successful. End-users, without having software development skills, are more likely to introduce software errors than professional programmers. In this study, we propose and encourage other DSML developers to extend the development of DSML with tool support. We believe the programming tools (e.g., debugger, testing tool, refactoring tool) are also needed for end-users to ensure the proper functioning of the products they develop. It is imperative that domain experts are provided with tools that work on the abstraction level that is familiar to them. In this paper, an industrial experience is presented for building various tools for usage in MDD. Debugger, automated testing infrastructure, refactoring, and other tools were implemented for Sequencer, a DSML. Our experience with the implementation of tool support for MDD confirms that these tools are indispensable for end-user programming in practice, and that implementing those tools might not be as costly as expected.


2009 ◽  
Vol 51 (8) ◽  
pp. 1244-1260 ◽  
Author(s):  
Georgia M. Kapitsaki ◽  
Dimitrios A. Kateros ◽  
George N. Prezerakos ◽  
Iakovos S. Venieris

Author(s):  
Siamak Farshidi ◽  
Slinger Jansen ◽  
Sven Fortuin

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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