# XLNet-Based Prediction Model for CVSS Metric Values
## 4 Experiments and Results
### Comparative Study
#### 4.3.3 Comparison with Other Similar Works  
In this study, the accuracy, precision, recall, and F1 of XLNET are compared to those of similar works. The results of other works were taken from the original papers [21; 22; 23]. Tables 8 and 9 display pertinent data. The results demonstrate that the fine-tuned XLNet enhances the performance of vulnerability metric value prediction. \begin{table}
\begin{tabular}{c l l l l l l l l} \hline \hline
**Metric** & **Model** & **AV** & **AC** & **PR** & **UI** & **S** & **C** & **I** & **A** \\ \hline \multirow{4}{*}{Accuracy} & XLNET & **0.9625** & 0.9562 & **0.9161** & **0.9440** & 0.9642 & **0.9303** & **0.9358** & **0.9423** \\  & BERT & 0.9580 & 0.9555 & 0.9094 & 0.9271 & 0.9532 & 0.9163 & 0.9229 & 0.9316 \\  & ROBERTA & 0.9610 & **0.9566** & 0.9148 & 0.9430 & **0.9654** & 0.9301 & 0.9350 & 0.9421 \\  & DISTILBERT & 0.9569 & 0.9565 & 0.9057 & 0.9245 & 0.9542 & 0.9156 & 0.9213 & 0.9307 \\ \hline \multirow{4}{*}{Precision} & XLNET & 0.8932 & 0.8614 & 0.8777 & **0.9405** & 0.9486 & 0.9132 & **0.9256** & 0.8927 \\  & BERT & **0.8964** & 0.8722 & 0.8749 & 0.9240 & 0.9328 & 0.8941 & 0.9073 & 0.9030 \\  & ROBERTA & 0.8890 & 0.8719 & **0.8779** & 0.9403 & **0.9535** & **0.9136** & 0.9245 & 0.9012 \\  & DISTILBERT & 0.8938 & **0.8867** & 0.8711 & 0.9206 & 0.9358 & 0.8950 & 0.9070 & **0.9130** \\ \hline \multirow{4}{*}{Recall} & XLNET & **0.8685** & **0.7791** & **0.8568** & **0.9386** & **0.9236** & **0.9030** & **0.9179** & **0.8336** \\  & BERT & 0.8363 & 0.7564 & 0.8386 & 0.9180 & 0.8988 & 0.8846 & 0.9027 & 0.8107 \\  & ROBERTA & 0.8571 & 0.7681 & 0.8512 & 0.9363 & 0.9230 & 0.9020 & 0.9167 & 0.8279 \\  & DISTILBERT & 0.8390 & 0.7502 & 0.8306 & 0.9159 & 0.8995 & 0.8819 & 0.8985 & 0.8071 \\ \hline \multirow{4}{*}{F1} & XLNET & **0.8800** & **0.8139** & **0.8662** & **0.9395** & **0.9355** & **0.9078** & **0.9214** & **0.8521** \\  & BERT & 0.8607 & 0.8017 & 0.8542 & 0.9208 & 0.9146 & 0.8891 & 0.9049 & 0.8309 \\ \cline{1-1}  & ROBERTA & 0.8717 & 0.8100 & 0.8633 & 0.9382 & 0.9373 & 0.9074 & 0.9204 & 0.8482 \\ \cline{1-1}  & DISTILBERT & 0.8616 & 0.8013 & 0.8476 & 0.9181 & 0.9164 & 0.8880 & 0.9025 & 0.8285 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the effects of the four pre-trained models on the CVSS v3.1 dataset. \begin{table}
\begin{tabular}{c l l l l l l l} \hline \hline
**Metric** & **Model** & **AV** & **AC** & **Au** & **C** & **I** & **A** \\ \hline \multirow{4}{*}{Accuracy} & XLNET & **0.9711** & **0.9167** & 0.9598 & **0.9072** & **0.9167** & **0.8946** \\  & BERT & 0.9692 & 0.9089 & 0.9591 & 0.8981 & 0.9105 & 0.8859 \\  & ROBERTA & 0.9708 & 0.9147 & **0.9600** & 0.9057 & 0.9159 & 0.8927 \\  & DISTILBERT & 0.9683 & 0.9075 & 0.9581 & 0.8966 & 0.9115 & 0.8847 \\ \hline \multirow{4}{*}{Precision} & XLNET & 0.9216 & 0.8714 & 0.7571 & **0.8837** & **0.8928** & **0.8736** \\  & BERT & 0.9195 & 0.8740 & **0.7622** & 0.8730 & 0.8854 & 0.8634 \\  & ROBERTA & 0.9214 & 0.8691 & 0.7612 & 0.8823 & 0.8922 & 0.8712 \\  & DISTILBERT & **0.9240** & **0.8764** & 0.7608 & 0.8727 & 0.8876 & 0.8630 \\ \hline \multirow{4}{*}{Recall} & XLNET & **0.8971** & **0.8106** & **0.7550** & **0.8831** & **0.8919** & **0.8750** \\  & BERT & 0.8877 & 0.7987 & 0.7431 & 0.8725 & 0.8842 & 0.8656 \\ \cline{1-1}  & ROBERTA & 0.8958 & 0.8045 & 0.7493 & 0.8800 & 0.8899 & 0.8718 \\ \cline{1-1}  & DISTILBERT & 0.8821 & 0.7932 & 0.7399 & 0.8685 & 0.8831 & 0.8624 \\ \hline \multirow{4}{*}{F1} & XLNET & **0.9086** & **0.8313** & **0.7560** & **0.8834** & **0.8923** & **0.8743** \\ \cline{1-1}  & BERT & 0.9022 & 0.8219 & 0.7521 & 0.8728 & 0.8848 & 0.8644 \\ \cline{1-1}  & ROBERTA & 0.9077 & 0.8258 & 0.7550 & 0.8811 & 0.8910 & 0.8715 \\ \cline{1-1}  & DISTILBERT & 0.9004 & 0.8170 & 0.7497 & 0.8705 & 0.8853 & 0.8626 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the effects of the four pre-trained models on the CVSS v2 dataset.