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

AGUSTÍN CASAMAYOR; DANIELA GODOY; MARCELO CAMPO

INFORMATION AND SOFTWARE TECHNOLOGY

ELSEVIER SCIENCE BV

Año: 2010 vol. 52 p. 436 - 445

0950-5849

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 is 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. In this paper, we propose a semisupervised approach which bases classification on a reduced number of categorized requirements by taking advantage of the knowledge provided by uncategorized ones, as well as certain properties of text. Thus, a small number of requirements, possibly identified by the requirement team during the elicitation process, enables learning an initial classifier for NFRs, which could successively identify the type of further requirements in an iterative process. Experimental results show that the proposed semi-supervised approach outperforms previous supervised classification proposals and can be enhanced by exploiting feedback provided by analysts.