Universiti Teknologi Malaysia Institutional Repository

An automated approach for identification of non-functional requirements using Word2Vec model

Younas, M. and Wakil, K. and Jawawi, D. N. A. and Shah, M. A. and Mustafa, A. (2019) An automated approach for identification of non-functional requirements using Word2Vec model. International Journal of Advanced Computer Science and Applications, 10 (8). pp. 539-547. ISSN 2158-107X


Official URL: http://dx.doi.org/10.14569/ijacsa.2019.0100871


Non-Functional Requirements (NFR) are embedded in functional requirements in requirements specification docu-ment. Identification of NFR from the requirement document is a challenging task. Ignorance of NFR identification in early stages of development increase cost and ultimately cause the failure of the system. The aim of this approach is to help the analyst and designers in architect and design of the system by identifying NFR from the requirements document. Several supervised learning-based solutions were reported in the literature. However, for accu-rate identification of NFR, a significant number of pre-categorized requirements are needed to train supervised text classifiers and system analysts perform the categorization process manually. This study proposed an automated semantic similarity based approach which does not needs pre-categorized requirements for identification of NFR from requirements documents. The approach uses an application of Word2Vec model and popular keywords for identification of NFR. Performance of approach is measured in term of precision-recall and F-measure by applying the approach to PROMISE-NFR dataset. The empirical evidence shows that the automated semi-supervised approach reduces manual human effort in the identification of NFR.

Item Type:Article
Uncontrolled Keywords:identification, non-functional requirements, semantic similarity
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:90765
Deposited By: Narimah Nawil
Deposited On:30 Apr 2021 22:30
Last Modified:30 Apr 2021 22:30

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