## 4 Materials and Methods
### Proto-MAML
#### 4.3.2 Proto-MAML Training Within a BERT-Enhanced QA Framework
Unlike state-of-the-art approaches, which are limited to a few languages (e.g., C and C++) and cover only 3-7 CWEs, Proto-MAML processed 9867 PHP samples, 4677 Java samples, 5019 C samples, and 4038 C++ samples, as well as addressing over 24 CWEs, therefore significantly broadening its practical utility. * _Interdisciplinary Collaboration:_ Through the utilization of a natural language-based approach, Proto-MAML facilitates effective interactions among development, operations, and security teams. The outputs in the form of questions and answers are easily interpretable, eliminating the need for additional analysis and fostering seamless communication across disciplines. * _Continuous Security Integration:_ Incorporating security evaluations as a core component of CI/CD pipelines ensures consistent code assessment, enhancing integrity and security. Proto-MAML excels in this area due to its low computational complexity (\(O(n\log n)\)), enabling dynamic security assessments without significantly impacting deployment times. It also provides advanced metrics such as Accuracy, Recall, F1-Score, and EM, which support its continuous performance improvement. * _Real-Time Monitoring and Auditing:_ The prototype-based architecture of Proto-MAML allows for rapid adaptation to new code samples by leveraging the general characteristics of queries (\(Q_{V}\)). This adaptability makes it ideal for dynamic systems with limited data, as it extracts rich semantic context (\(C_{V}\)) and efficiently adjusts to emerging vulnerabilities. * _Predictive Capability:_ Proto-MAML accurately identifies the specific positions of vulnerabilities within code (\(A_{V[START]}\), \(A_{V[END]}\)), simplifying traceability and auditing processes. This actionable and precise information significantly enhances the ability of technical teams to efficiently address vulnerabilities. * _Training and Awareness in Security:_ Through analyzing the semantic context of source code using BERT, Proto-MAML identifies insecure dependencies and generates clear predicted responses (\(A^{\prime}_{V}\)). This functionality is invaluable for detecting issues in third-party libraries or frameworks, not only preventing vulnerabilities but also educating development teams on secure coding practices, allowing for the seamless integration of this knowledge into daily workflows. * _Dependency and Third-Party Component Management:_ Proto-MAML evaluates external dependencies for vulnerabilities and provides practical solutions while explaining the associated context. Its ability to handle underrepresented languages such as PHP--which constitutes 30% of insecure web deployments--along with its focus on over 24 CWEs makes it highly scalable and adaptable to diverse environments. Additionally, its contextual approach allows for its application across different programming languages, as it relies on semantic context rather than specific code structures. Table 13 provides a comprehensive comparison of Proto-MAML against state-of-the-art models, in terms of their limitations and capabilities. This comparison highlights the strengths of Proto-MAML, including its low computational complexity, adaptability to new vulnerabilities, and ability to handle diverse programming languages and CWEs. Unlike other approaches, Proto-MAML enables superior interdisciplinary collaboration, can be efficiently integrated into CI/CD pipelines, and demonstrates actionable predictive capabilities, setting a benchmark for real-world applications in SSDLC and DevSecOps environments.