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Identifying architectural concerns from non-functional requirements using support vector machine

Gökyer, Gökhan
There has been no commonsense on how to identify problem domain concerns in architectural modeling of software systems. Even, there is no commonly accepted method for modeling the Non-Functional Requirements (NFRs) effectively associated with the architectural aspects in the solution domain. This thesis introduces the use of a Machine Learning (ML) method based on Support Vector Machines to relate NFRs to classified "architectural concerns" in an automated way. This method uses Natural Language Processing techniques to fragment the plain NFR texts under the supervision of domain experts. The contribution of this approach lies in continuously applying ML techniques against previously discovered “NFR - architectural concerns” associations to improve the intelligence of repositories for requirements engineering. The study illustrates a charted roadmap and demonstrates the automated requirements engineering toolset for this roadmap. It also validates the approach and effectiveness of the toolset on the snapshot of a real-life project.