|Authors||P. H. Nguyen, M. E. Kramer, J. Klein and Y. Le Traon|
|Title||An Extensive Systematic Review on the Model-Driven Development of Secure Systems|
|Afilliation||Software Engineering, Software Engineering|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Journal||Information and Software Technology|
|Keywords||MDE, MDS, model transformations, model-driven engineering, model-drivensecurity, software security engineering, survey, systematicreview|
Context: Model-Driven Security (MDS) is as a specialised Model-Driven Engineering research area for supporting the development of secure systems. Over a decade of research on MDS has resulted in a large number of publications.
Objective: To provide a detailed analysis of the state of the art in MDS, a systematic literature review (SLR) is essential.
Method: We conducted an extensive SLR on MDS. Derived from our research questions, we designed a rigorous, extensive search and selection process to identify a set of primary MDS studies that is as complete as possible. Our three-pronged search process consists of automatic searching, manual searching, and snowballing. After discovering and considering more than thousand relevant papers, we identified, strictly selected, and reviewed 108 MDS publications.
Results: The results of our SLR show the overall status of the key artefacts of MDS, and the identified primary MDS studies. E.g. regarding security modelling artefact, we found that developing domain- specific languages plays a key role in many MDS approaches. The current limitations in each MDS artefact are pointed out and corresponding potential research directions are suggested. Moreover, we categorise the identified primary MDS studies into 5 principal MDS studies, and other emerging or less common MDS studies. Finally, some trend analyses of MDS research are given.
Conclusion: Our results suggest the need for addressing multiple security concerns more systematically and simultaneously, for tool chains supporting the MDS development cycle, and for more empirical studies on the application of MDS methodologies. To the best of our knowledge, this SLR is the first in the field of Software Engineering that combines a snowballing strategy with database searching. This combination has delivered an extensive literature study on MDS.