[Retracted] Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP

Table 5

Comparison of all the experiments.

Experiment	Process	Output
CNN and FastText embedding	CNN-based processing	Accuracy: 71.89%; precision: 0.88; recall: 0.72; F1-score: 0.77
Bidirectional LSTM with FastText embedding	Bidirectional GRU or LSTM with global attention	Accuracy: 84.33%; precision: 0.91; recall: 0.84; F1-score: 0.87
USE model	USE pretrained model with TF 1.0	Accuracy: 92.61%; precision: 0.95; recall: 0.93; F1-score: 0.93
NNLM	NNLM-based sentence encoder, with pretrained model	Accuracy: 90.16%; precision: 0.81; recall: 0.90; F1-score: 0.86
BERT	BERT tokenization and TF Keras modeling	Accuracy: 91.39%; precision: 0.92; recall: 0.91; F1-score: 0.88
DistilBERT	DistilBERT-based preprocessing of data	Accuracy: 94.77%; precision: 0.95; recall: 0.95; F1-score: 0.94
BERT	Data preprocessing and tokenization with BERT	Accuracy: 97.44%