Collaborative MDSE

This web page contains the manuscript, supplemental material and the replication package of the paper accepted to the IEEE Transactions on Software Engineering (TSE) journal with title:

“Collaborative Model-Driven Software Engineering: a Classification Framework and a Research Map”

This study has been designed, developed, and reported by the following investigators:

For any information, interested researchers can contact us by writing an email to any investigator listed above.

Accepted TSE manuscript

Supplemental materials


Collaborative Model-Driven Software Engineering (MDSE) consists of methods and techniques where multiple stakeholders manage, collaborate, and are aware of each others' work on shared models.
Collaborative MDSE is attracting research efforts from different areas, resulting in a variegated scientific body of knowledge. This study aims at identifying, classifying, and understanding existing collaborative MDSE approaches.
We designed and conducted a systematic mapping study. Starting from over 3,000 potentially relevant studies, we applied a rigorous selection procedure resulting in 106 selected papers, further clustered into 48 primary studies along a time span of 19 years. We rigorously defined and applied a classification framework and extracted key information from each selected study for subsequent analysis.
Our analysis revealed the following main findings:
(i) there is a growing scientific interest on collaborative MDSE in the last years;
(ii) multi-view modeling, validation support, reuse, and branching are more rarely covered with respect to other aspects about collaborative MDSE;
(iii) different primary studies focus differently on individual dimensions of collaborative MDSE (i.e., model management, collaboration, and communication);
(iv) most approaches are language-specific, with a prominence of UML-based approaches;
(v) few approaches support the interplay between synchronous and asynchronous collaboration.
This study gives a solid foundation for classifying existing and future approaches for collaborative MDSE. Researchers and practitioners can use our results for identifying existing research/technical gaps to attack, better scoping their own contributions, or understanding existing ones.

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