## 4 Materials and Methods
### Proto-MAML
#### 4.3.2 Proto-MAML Training Within a BERT-Enhanced QA Framework
The main theoretical contributions include:

* _Multilingual Vulnerability Detection_: Proto-MAML integrates Prototypical Networks and MAML within a QA-based architecture, enabling a single model to detect vulnerabilities across programming languages like PHP, Java, C, and C++. This approach adapts meta-learning principles to handle the structural and syntactic diversity of source code, which is a common challenge in multilingual environments. The theoretical impact lies in extending meta-learning to heterogeneous data domains. * _Precise Vulnerability Localization_: Leveraging the QA framework, the model identifies the exact start and end positions of vulnerabilities within the source code. This level of detail connects theoretical advancements in meta-learning with practical needs for targeted code review and correction. It provides a bridge between machine learning outputs and actionable developer workflows. * _Generalization in Data-Scarce Scenarios_: Proto-MAML uses FSL to train effectively with minimal annotated data, addressing a critical limitation in vulnerability datasets. The contribution lies in demonstrating how meta-learning can generalize to real-world problems where comprehensive training datasets are unavailable, a common constraint in software security. * _Integration of Detection and Remediation_: The model not only identifies vulnerabilities but also provides contextual suggestions for remediation. This feature aligns theoretical advancements with practical applications by connecting model outputs to actionable developer tasks, making the process of addressing vulnerabilities more efficient. * _Efficiency for CI/CD Integration_: The computational complexity of \(O(n\log n)\) ensures that Proto-MAML is scalable and compatible with time-sensitive CI/CD pipelines. This shows how meta-learning models can be designed to meet the operational constraints of modern software development environments. These theoretical contributions address specific challenges in vulnerability detection and correction, demonstrating how meta-learning can be applied to software security. By linking detection with actionable insights and ensuring scalability, Proto-MAML provides a framework that can guide future advancements in secure software development. ### Practical Contributions

The experimental evaluation demonstrates that Proto-MAML achieves high performance across PHP, Java, C, and C++:

* Average Precision, Recall, F1, and EM scores exceeded 98.49%, 98.54%, and 98.78%, respectively. * PHP achieved the highest metrics, with 99.93% F1 and 99.82% EM scores. * The computational complexity of \(O(n\log n)\) ensures its practical application in real-time CI/CD workflows. The model enhances collaboration across development, security, and operations teams by providing precise, actionable outputs. This integration supports the goals of SSDLC and DevSecOps practices. ### Future Research Directions

Further research could expand and refine Proto-MAML in the following areas:

* _Expanding Language Support_: Including additional languages such as Python, JavaScript, and Go through transfer learning techniques. * _Improving Explainability_: Enhancing interpretability by linking vulnerabilities to specific code regions and providing clear rationales for remediation. * _Real-World Deployment_: Evaluating the model in operational CI/CD environments to refine its practical applications. * _Continuous Learning_: Enabling incremental adaptation to emerging vulnerabilities and evolving coding practices. **Author Contributions:** Conceptualization, A.H.-S., G.S.-P., L.K.T.-M., H.P.-M., J.P.-P., J.O.-M., P.C.-F., and L.J.G.V. ; methodology, A.H.-S., G.S.-P., L.K.T.-M., H.P.-M., [PERSON]-P., [PERSON]-M., P.C.-F., and L.J.G.V. ; validation, [PERSON]-S., [PERSON]-P., [PERSON]-M., [PERSON]-M., [PERSON]-P., [PERSON]-M., P.C.-F., and L.J.G.V. ; formal analysis, [PERSON]-S., [PERSON]-P., [PERSON]-M., [PERSON]-M., [PERSON]-P., [PERSON]-M., P.C.-F., and L.J.G.V.