Using Machine Learning for Non-Functional Requirements Classification: A Practical Study

  • Daniel Abella C. M. de Souza UFCG
  • Danyllo Albuquerque UFCG
  • Emanuel Dantas Filho IFPB
  • Mirko Perkusich UFCG
  • Angelo Perkusich UFCG


Non-Functional Requirements (NFR) are used to describe a set of software quality attributes such as reliability, maintainability, and performance. Since the functional and non-functional requirements are mixed together in software documentation, it requires a lot of effort to distinguish them. This study proposed automatic NFR classification by using machine learning classification techniques. An empirical study with three machine learning algorithms was applied to classify NFR automatically. Precision, recall, F1-score, and accuracy were calculated for the classification results through all techniques. The results showed that the SGD SVM classifier achieves the best results where precision, recall, F1-score, and accuracy reported were 0.66, 0.61, and 0.61.

Palavras-chave: Non-Functional Requirements, Classification, Machine Learning, Text Analysis


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SOUZA, Daniel Abella C. M. de; ALBUQUERQUE, Danyllo; DANTAS FILHO, Emanuel; PERKUSICH, Mirko; PERKUSICH, Angelo. Using Machine Learning for Non-Functional Requirements Classification: A Practical Study. In: WORKSHOP BRASILEIRO DE ENGENHARIA DE SOFTWARE INTELIGENTE (ISE), 3. , 2023, Campo Grande/MS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 35-38. DOI: