The early detection and classification 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 classification of NFRs from textual requirements descriptions. Classification relies on a reduced number of categorized requirements

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... d 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.