The Effects of Vectorization Methods on Non-Functional Requirements Classification

Abstract

CONTEXT: Architecture and design of systems are sensitive to non-functional requirements (NFRs). Identifying NFRs and their categories at early phase is an essential task for project success. Automatic classification methods for that purpose have been studied for supporting requirement analysis. The past studies used simple vectorization methods and might miss semantics and interactions among words in requirements. OBJECTIVE: To examine whether different vectorization methods lead to differences in the classification performance of NFRs and their categories. METHOD: Comparative experiments were conducted with open data. Five vectorization methods including document embedding methods and four supervised classification methods were supplied. RESULTS: Some advanced methods could achieve better performance than traditional ones. The preference was dependent on classification methods. CONCLUSIONS: It is beneficial to consider using advanced methods for classifying non-functional requirements categories.