A recommender system for classification of non-functional requirements

AGUSTÍN CASAMAYOR; DANIELA GODOY; MARCELO CAMPO

Mar del Plata

Simposio; 10th Argentine Symposium on Artificial Intelligence (ASAI'2009); 2009

SADIO

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 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.