Identification of non-functional requirements in textual specifications: a semi-supervised learning approach

• Autores: Agustín Casamayo, Daniela Godoy, Marcelo Campo
• Localización: Information and software technology, ISSN 0950-5849, Vol. 52, Nº 4, 2010, págs. 436-445
• Idioma: inglés
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• Resumen

  • Context Early detection of non-functional requirements (NFRs) is crucial in the evaluation of architectural alternatives starting from initial design decisions. The application of supervised text categorization strategies for requirements expressed in natural language has been proposed in several works as a method to help analysts in the detection and classification of NFRs concerning different aspects of software. However, a significant number of pre-categorized requirements are needed to train supervised text classifiers, which implies that analysts have to manually assign categories to numerous requirements before being able of accurately classifying the remaining ones.