Automated transition from use cases to UML state machines to support state-based testing
ECMFA'11 Proceedings of the 7th European conference on Modelling foundations and applications
Functional requirements validation by transforming use case models into Abstract State Machines
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Using machine learning to enhance automated requirements model transformation
Proceedings of the 34th International Conference on Software Engineering
ECMFA'12 Proceedings of the 8th European conference on Modelling Foundations and Applications
Facilitating the transition from use case models to analysis models: Approach and experiments
ACM Transactions on Software Engineering and Methodology (TOSEM)
Transforming and tracing reused requirements models to home automation models
Information and Software Technology
Conceptual modeling of natural language functional requirements
Journal of Systems and Software
Hi-index | 0.00 |
Model transformation is one of the basic principles of Model Driven Architecture. To build a software system, a sequence of transformations is performed, starting from requirements and ending with implementation. However, requirements are mostly in the form of text, but not a model that can be easily understood by computers; therefore, automated transformations from requirements to analysis models are not easy to achieve. The overall objective of this systematic review is to examine existing literature works that transform textual requirements into analysis models, highlight open issues, and provide suggestions on potential directions of future research. The systematic review led to the analysis of 20 primary studies (16 approaches) obtained after a carefully designed procedure for selecting papers published in journals and conferences from 1996 to 2008 and Software Engineering textbooks. A conceptual framework is designed to provide common concepts and terminology and to define a unified transformation process. This facilitates the comparison and evaluation of the reviewed papers.