Security Requirements Classification into Groups Using NLP Transformers
Vasily Varenov, Aydar Gabdrahmanov · 2021
This study presents an implementation of sentencelevel classification of security requirements into predefined groups. The method of this paper suggests using three models: BERT, XLNET, and DistilBERT for classification task and figures out evaluation metrics such as precision, recall, and F1-score. We compiled a new dataset of 1086 security requirements of 7 classes collected from multiple existing datasets, such as PURE, SecReq and Riaz's dataset. The best-achieved result is DistilBERT’s 78% F1-score on the multiclass classification task. The main contribution of this study is the new multiclass dataset of security requirements and an example of how a deep transformer model can be used for requirements elicitation, which can be used as a basis for further improvement.