## 4 Materials and Methods
### Proto-MAML
#### 4.3.2 Proto-MAML Training Within a BERT-Enhanced QA Framework
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Automated Security Scanning** & **Interdisciplinary Collaboration** & **Confinuous Security Integration** & **Real-Time Monitoring and Aading** & **Predictive Capability** & **Dependency and Component Management** \\ \hline \multirow{4}{*}{Proo-MAML (this study)} & FRI capabilities to detect vulnerabilities across FIFI, over 24 C/AUs, covering over 24 C/AUs, Providable accounts, & Findings in interpretable QA outputs. & Low complexity (\(\mathcal{O}^{n}\log n\)), enabling anomalies & Adapts dynamically to new samples based on rich semantics (\(\mathcal{C}_{\mathrm{f}}\)). & Accurately identifies & Identifying insecure dependencies and generates practical solutions. \\ \cline{2-7}  & Graph dependencies (AST, PIC) increase complexity & Graphs require advanced supervision, handling acquisition, & Graph preprocessing slows & Static nature impulse adaptation to emerging & Cannot product price & Does not evaluate external spans within code due to dependency or insecure frameworks. \\ \cline{2-7}  & Limited to Cand C+-ticks coverage for critical implementations based upon re-technical teams. & Fixed linearization hinders the target implementation by non-technical teams. & Dense attention layers & Cannot dynamically & Locally precise open & Igores external dependencies and effectiveness. \\ \cline{2-7}  & Only addressed 3-6 CWEs, with limited detection in emerging vulnerabilities. & Probabilistic modeling produces ambiguous results, completing collaboration & prevents dynamic adjustments (\(\mathcal{O}^{n}\)), complexity reduction & Requires intensive returns to a new patterns to a new monitoring. & Does not predict spans, rules on rigid templates for hard analytics. & Does not evaluate external dependencies or insecure frameworks. \\ \hline \multirow{4}{*}{ValDefalCoord} & High complexity (\(O^{n}\)) & Dependence on graphs and recurrent networks & Heavy computational & Requires on pre-labeled data, demands limit automated replayping in integration, collaboration & Highly precise, but extreme & Requires on pre-labeled labels, complexity reduces & Requires on pre-labeled labels, complexity reduction & Requires on pre-labeled labels, training it unsuitable for external dependencies or unreported concepts. \\ \cline{2-7}  & Restrict to Java with low & Sequential search outputs & Iterative searches delay & Icariness reduces delay & Icariness reduces delay & Icariness that-party dependencies, unsuitable for non-valid multilingual environments. \\ \cline{2-7}  & CWE coverage; IRS (\(O^{n}\)) & Our coverage; IRS (\(O^{n}\)) & Are difficult to interpret and contextualize. & & & & \\ \cline{2-7}  & & & & & & \\ \cline{2-7}  & & & & & & \\ \cline{2-7}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13. Limitations of State-of-the-Art Models Versus Proto-MAML for SSDLC and DevSecOps Integration. \begin{table}
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## 7 Conclusions

This study presented Proto-MAML, an approach that integrates Prototypical Networks with Model-Agnostic Meta-Learning within a QA framework based on BERT. Proto-MAML addresses challenges related to detecting and remediating vulnerabilities in source code across multiple programming languages, specifically PHP, Java, C, and C++. Leveraging FSL and prototype-based meta-learning, the model adapts efficiently to limited data, making it applicable to the dynamic and resource-constrained environments common in SSDLC and DevSecOps practices. ### Theoretical Contributions

Proto-MAML advances the theoretical understanding of how meta-learning can be applied to detecting, reviewing, and correcting vulnerabilities in source code. These contributions are significant due to their focus on addressing core challenges specific to software security.