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Article Information:
Optimizing Support Vector Machine for Classifying Non Functional Requirements
K. Mahalakshmi, R. Prabhakar and V. Balakrishnan
Corresponding Author: K. Mahalakshmi
Submitted: November 22, 2014
Accepted: December 04, 2013
Published: May 05, 2014 |
Abstract:
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Problems faced in contemporary practice should be understood to improve requirements engineering processes. System requirements are descriptions of services provided by a system and operational constraints. Non-Functional Requirements (NFR) defines overall qualities/attributes of the system. NFR analysis is a significant activity in this branch of engineering. In this study, a methodology for classifying NFR is presented. Inverse Document Frequency is used for extracting the features from the NFR dataset and is classified by Support Vector Machine (SVM). The efficiency of the SVM depends upon the parameter used with Radial Basis Function. In this study, the RBF kernel is optimized by Artificial Bee Colony algorithm (ABC) to optimize the RBF parameters to improve performance.
Key words: Artificial Bee Colony algorithm (ABC), functional requirements, Non-Functional Requirements (NFR), requirement engineering, Support Vector Machine (SVM), ,
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Cite this Reference:
K. Mahalakshmi, R. Prabhakar and V. Balakrishnan, . Optimizing Support Vector Machine for Classifying Non Functional Requirements. Research Journal of Applied Sciences, Engineering and Technology, (17): 3643-3648.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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