## 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
**Framework** & **Quantitative Metrics (F1, TP, FP)** & **Qualitative Adaptability** & **False-Positive Handling and Observations** \\ \hline Proto-MAML (This Study) & F1: 98.78\%, TP: 97.8\%, FP: 2.2\% & High adaptability due to meta-learning loops. Supports diverse languages and CWEs with minimal data. Well-suited for incremental learning scenarios. & False positives reduced through precise query alignment in the support set \(\mathcal{S}\). Avoids overfitting via regularized parameter updates. \\ \hline SyeVR [27] & F1: 85.8\%, TP: 85.1\%, FP: 14.2\% & Limited adaptability. Relies on extensive labeled data sets, constraining performance in unseen languages or CWEs. & High false-positive rate due to static AST generation, which misses dynamic code behaviors. \\ \hline BBVD [29] & F1: 95.42\%, TP: 90.5\%, FP: 5.1\% & Moderate adaptability via RoBERTa fine-tuning, but struggles with heterogeneous code due to fixed tokenization schemes. & False positives are controlled using multiple attention layers, but performance degrades when using highly nested code structures. \\ \hline VulDefend [30] & F1: 89.9\%, TP: 86.2\%, FP: 7.8\% & Moderate adaptability through RoBERTa and PET. Effective in low-data settings but lacks flexibility for unseen CWEs. & False positives are from probabilistic template errors during few-shot adaptation. \\ \hline VulDecIocator [28] & F1: 98.8\%, TP: 98.2\%, FP: 1.8\% & High adaptability for C code with AST and SSA. Limited when applied to other languages. & False positives minimized through sequential learning and SSA optimizations but requires costly preprocessing. \\ \hline SeqTrans [31] & Masked Correction: 25.3\%, TP: 62.1\%, FP: 25.9\% & Poor adaptability due to reliance on sequential generation. BS struggles with multi-pattern code. & High false-positive rate, as BS favors common sequences over precise mappings. \\ \hline VRepair [32] & Reconstruction Rate: 27.59\%, TP: 72.4\%, FP: 12.3\% & Limited adaptability due to heavy reliance on static TL. Struggles with evolving or unseen vulnerabilities. & Moderate false positives due to dependency on sequential context without incorporating dynamic code behavior. \\ \hline DB-CBIL [33] & Reconstruction Rate: 99.51\%, TP: 99.0\%, FP: 8.3\% & Strong adaptability through CNN and BiLSTM integration, but computationally intensive. & False positives mitigated by token-level sequence labeling, but not fully eliminated in abstract syntax scenarios. \\ \hline VuRLE [34] & Replacement Prediction: 65.59\%, TP: 65.0\%, FP: 7.2\% & Poor adaptability due to reliance on static AST and DBSCAN clustering, which fail in sparse data environments. & False positives arise from inaccuracies in the clustering performed by DBSCAN on skewed data sets. \\ \hline VulD-Transformer [35] & F1: Range 59.34–80.44\%, TP: 12.0\% & Moderate adaptability via multi-attention transformers, but limited by PDG preprocessing bottlenecks. & False positives reduced by attention mechanisms, but increases with complex PDG structures. \\ \hline GPT Survey [36] & Accuracy: 88.0\%, TP: 88.0\%, FP: 12.0\% & High adaptability in query handling, but inconsistent outputs depending on fine-tuning and batch size. & False positives fluctuate based on query specificity and model calibration. \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Qualitative and Quantitative Comparison of Vulnerability Detection Models. questions (\(Q_{V}\)) and answers (\(A_{V}\)) based on semantic context (\(C_{V}\)). This capability enables early vulnerability detection and the provision of concrete solutions through secure code reconstructions. Furthermore, Proto-MAML is applicable to multiple programming languages and addresses a range of critical vulnerabilities.