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 &ldquo / NFR -
architectural concerns&rdquo / 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.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609964/index.pdf |
| Date | 01 August 2008 |
| Creators | Gokyer, Gokhan |
| Contributors | Sener, Cevat |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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