Open this publication in new window or tab >>2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]
Collaborative model-driven software engineering addresses the complexities of developing software systems by prioritizing models as core artifacts and leveraging the collective expertise of diverse stakeholders. To effectively realize this approach, the employed modeling environments must be equipped with features that support and enhance collaboration. These environments should, among other capabilities, provide support for multiple notation types, enabling stakeholders to engage with models using their preferred notation or the notation most appropriate for their tasks. Additionally, they should offer multiple views and perspectives that allow stakeholders to interact with pertinent information only, and implement access control mechanisms to ensure information security. However, the adoption of these features can be challenging, partly because of their resource-intensive and tedious development nature, as well as the necessity for continuous updates to keep up with the evolution of modeling languages. This doctoral thesis proposes a model-driven approach to address this challenge by facilitating the development of blended modeling environments featuring multiple views and ensuring modeled information security. The proposed framework leverages automation to reduce the manual effort and expertise traditionally required for i) the provision of synchronization mechanisms between graphical and textual notations for blended modeling, ii) the provision of synchronization mechanisms between view models and base model in multi-view modeling, and iii) the consistent definition and enforcement of access permissions. This research, therefore, lowers the barriers to adopting these collaborative features by facilitating their development and evolution in face of changes to underlying modeling languages.
Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2024. p. 290
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 408
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-66541 (URN)978-91-7485-649-1 (ISBN)
Public defence
2024-06-17, Gamma, Mälardalens universitet, Västerås, 13:15 (English)
Opponent
Supervisors
2024-05-072024-05-072024-11-14Bibliographically approved