## 4 Materials and Methods
### Proto-MAML
#### 4.3.2 Proto-MAML Training Within a BERT-Enhanced QA Framework
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Language** & **Values of \(i\) Analyzed** & **Optimal Value of \(i\) (Explanation)** & **Statistic \(W\) (Significance)** & \(p\)**-Value** \\ \hline \multirow{4}{*}{PHP} & \multirow{4}{*}{\(5,6,7,8,9,10\)} & The optimal value of \(i\) was 9. When increasing \(i\) to 9, the performance metrics (\(\mathrm{Precision}_{\mathrm{combined}}\), \(\mathrm{Recall}_{\mathrm{combined}}\), \(\mathrm{F1}_{\mathrm{combined}}\)) reached their highest values, indicating better classification accuracy. & Positive differences predominate, supporting \(i=9\). & Less than 0.05 \\ \hline \multirow{4}{*}{Java} & \multirow{4}{*}{\(5,6,7,8,9,10\)} & The optimal value of \(i\) was 10. Continuous improvements in the metrics were observed, reaching the highest values at \(i=10\). This captures the complexity of the language better. & Negative differences predominate, supporting \(i=5\). & Less than 0.05 \\ \cline{1-1} \cline{2-4}  & & The optimal value of \(i\) was 5. Increasing \(i\) beyond 5 did not improve metrics and sometimes decreased them, suggesting that \(i=5\) is sufficient for classification. & Negative differences predominate, supporting \(i=5\). & Less than 0.05 \\ \hline \multirow{4}{*}{C++} & \multirow{4}{*}{\(5,6,7,8,9,10\)} & The optimal value of \(i\) was 8. Performance improved up to \(i=8\), but showed no significant improvement beyond that value. & Positive differences predominate, supporting \(i=8\). & Less than 0.05 \\ \cline{1-1} \cline{2-4}  & & The optimal value of \(i\) was 8. Performance improved up to \(i=8\), but showed no significant improvement beyond that value. & Positive differences predominate, supporting \(i=8\). & Less than 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of WSR Results for Each Programming Language. Signed-Rank Test, highlight statistically significant variations in the performance metrics as \(i\) fluctuates, therefore substantiating the selection of optimal configurations. These results highlight the need to customize models to the intricate nuances inherent to each programming language, thus ensuring peak efficacy. Building upon these refined configurations, distinct models were constructed for each language, facilitating an exhaustive evaluation of the Proto-MAML framework utilizing residual samples from \(X_{P}\). Table 9 encapsulates these insights, delineating the metrics \(\text{Precision}_{\text{combined}}\), \(\text{Recall}_{\text{combined}}\), \(F1_{\text{combined}}\), and EM. ## 6 Discussion

To highlight the advantages of Proto-MAML compared to the state-of-the-art works discussed in Section 3, three key points were identified that emphasize the importance of ML operations in reviewing and correcting vulnerable source code, as outlined in the following paragraphs. **First point of comparison: number of samples and language coverage. ** Proto-MAML efficiently reviews vulnerable code with fewer samples, leveraging prototype-based learning to mimic human-like gradual generalization, providing succinct responses that are necessary to maintain alignment with the workflows of analysts in the SSDLC context when addressing scenarios involving limited code. With fewer than \(18,879\) samples across PHP, Java, C, and C++, covering 24 critical CWEs, Proto-MAML can address vulnerabilities such as buffer overflows, code injection, cross-site request forgery, remote code execution, and SQL injection, among others. This makes it highly suitable for fast-paced environments where large-scale labeling is not practical. In contrast, other state-of-the-art studies have focused on fewer languages and typically used larger data sets. For example, the samples used for Proto-MAML included 9867 for PHP, 4677 for Java, 5019 for C, and 4038 for C++, addressing over 24 CWEs.