Semi-Supervised Classification of Non-Functional Requirements: An Empirical Analysis

• Autores: Agustín Casamayor, Daniela Godoy, Marcelo Campo
• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 13, Nº. 44, 2009, págs. 35-44
• Idioma: inglés
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• Resumen

  • The early detection and classiffication of non-functional requirements (NFRs) is not only a hard and time consuming process, but also crucial in the evaluation of architectural alternatives starting from initial design decisions. In this paper, we propose a recommender system based on a semi-supervised learning approach for assisting analysts in the detection and classiffication of NFRs from textual requirements descriptions. Classiffication relies on a reduced number of categorized requirements and takes advantage of the knowledge provided by uncategorized ones as well as certain properties of text. Experimental results show that the proposed recommendation approach based on semi-supervised learning outperforms previous proposals for classifying different types of requirements.