## 4 Materials and Methods
### Proto-MAML
#### 4.3.2 Proto-MAML Training Within a BERT-Enhanced QA Framework
Additionally, this approach struggles with inaccuracies when processing large data sets due to its reliance on token-level matching. Proto-MAML overcomes these inefficiencies with its prototype-based meta-learning approach, achieving a 99.5% match rate for Java code, thus vastly outperforming the 25.3% match rate of SeqTrans. Turning to VRepair, [32] its use of an SNN and SSA results in iterative operations with an overall complexity of \(O(n^{4})\). The SNN processes sequential data with \(O(n^{2})\) complexity, while SSA adds substantial overhead through analyzing the control flow. Proto-MAML avoids these resource-intensive operations by dynamically adapting features across support and query sets and achieved an exact match rate of 98.2% for C code, representing a dramatic improvement over the 27.59% match rate of VRepair. Similarly, DB-CBIL [33] combines DistilBERT with a CNN and BiLSTM. Although DistilBERT is lightweight, the integration of CNN and BiLSTM architectures elevates its complexity to \(O(n^{4})\). These processes lack the adaptability required to generalize effectively across diverse vulnerability patterns. Proto-MAML, leveraging its structured meta-learning framework, achieved a comparable match rate of 97.9% for C++ code while maintaining a more efficient complexity of \(O(n\log n)\). Furthermore, VuRLE, as outlined in [34], incorporates AST and DBSCAN, resulting in an overall complexity of \(O(n^{5})\). The hierarchical structure of AST and the clustering operations of DBSCAN are computationally prohibitive, making this approach unsuitable for large-scale applications. In contrast, Proto-MAML relies on prototype-based learning, thus eliminating these inefficiencies. Proto-MAML achieved a match rate of 99.5% for Java code, significantly surpassing the 65.59% match rate of VuRLE. The GPT Survey [36] presents a distinct approach, leveraging large-scale pre-trained models for vulnerability detection. However, its reliance on fine-tuning for specific queries introduces significant variability in complexity, ranging from \(O(n^{2})\) for simpler queries to \(O(n^{3})\) for more intricate ones. This dependence on extensive calibration processes increases the computational overhead, particularly in scenarios requiring complex query adjustments or knowledge generation. Additionally, the closed-source nature of this approach limits its adaptability to specific tasks, such as identifying vulnerabilities in diverse programming languages. In comparison, Proto-MAML employs structured meta-learning cycles to avoid iterative fine-tuning, enabling efficient adaptation across various support-query tasks. This resulted in a consistent performance advantage, with Proto-MAML achieving an average accuracy of 97.9%, significantly exceeding the 88% accuracy of the GPT Survey. Furthermore, the complexity of \(O(n\log n)\) provided by Proto-MAML offers a scalable and efficient framework for real-world applications, solidifying its superiority in balancing performance and computational efficiency. Notably, two exceptions where other models surpassed Proto-MAML stand out: DB-CBIL [33] achieved a reconstruction rate of 99.51%, compared to the exact match rate of 97.9% achieved by Proto-MAML, but with a higher complexity of \(O(n^{4})\). Similarly, VulDeeLocator [28] outperformed Proto-MAML in terms of accuracy, reporting 98.8% compared to the 97.1% accuracy of the proposed approach, although it also had a higher complexity of \(O(n^{4})\). For Java, Proto-MAML surpassed the results reported for VuRLE [34], which had a transformation rate of 65.59%, and SeqTrans [31], which achieved a masked correction rate of 25.3%, compared to the exact match rate of 99.5% achieved by Proto-MAML. In the same vein, qualitative adaptability and false-positive handling are critical dimensions when evaluating ML approaches for their application-specific utility, as highlighted in the qualitative analysis of DL methods [59].