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

Abstract

Context:
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.
Objective:
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.
Method:
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.
Results:
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.
Conclusion:
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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