Abstract
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.
Highlights
Requirements Engineering (RE) is a software engineering branch dealing with real-world goals for, functions of and software systems (Aljahdali et al, 2011) constraints
NonFunctional Requirements (NFR) dataset available in the promise data repository is used to evaluate the proposed methodology
When RE is taken as a continual task in the project, RE process model is iterative and its activities ensued across multiple phases, making process models seem iterative
Summary
Requirements Engineering (RE) is a software engineering branch dealing with real-world goals for, functions of and software systems (Aljahdali et al, 2011) constraints. This process is arguably a most important process in software development. Functional requirements confusion affects product’s functionality requiring that inconsistency be removed at the beginning. As functional requirements deal with specific issues, they are implemented through specific localized modules/components (Cysneiros and Yu, 2004). Though stated informally, they can be formalized when needed
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