## 3 Related Works  
The detection and remediation of software vulnerabilities have advanced significantly through various machine learning techniques and estimators. Among the early frameworks, SySeVR [27] combined syntactic and semantic representations of C/C++ code with Recurrent Neural Networks (RNNs), including Long Short-Term Memory (LSTM) models. Using Abstract Syntax Trees (ASTs) and Program Dependency Graphs (PDGs), SySeVR generated code slice vectors to identify secure and insecure regions, analyzing over 56,500 secure and 340,000 insecure segments extracted from 14,780 snippets. By integrating LSTM and Bidirectional Gated Recurrent Unit (BGRU) layers, the framework achieved an F1-score of 85.8%. Building on RNN-based approaches, VulDeeLocator [28] extended VulDeePecker by leveraging Natural Language Processing (NLP) and RNN methods to detect vulnerabilities in C code. It employed ASTs, segmentation, and Static Single Assignment (SSA) representations to capture data dependencies and control flows across functions and files. The processed information was integrated into a Bi-RNN with LSTM layers, achieving an accuracy of 98.8% using approximately 29,000 samples. Transformer-based architectures have further improved vulnerability detection. BBVD (BERT-Based Vulnerability Detection) [29] utilized pre-trained BERT models (e.g., RoBERTa, DistilBERT, and MobileBERT) to analyze over 16,436 code sections in C/C++. Tokenizers and byte-pair encoding were used to fine-tune the model, achieving up to 95.42% accuracy in classifying segments as safe or unsafe. This method targeted vulnerabilities such as arithmetic errors, pointer references, arrays, and Application Programming Interface (API) calls. Similarly, VulDefend [30] adopted Pattern-Exploiting Training (PET) and cloze-style masking with RoBERTa for detecting vulnerabilities in C and C++ code. VulDefend replaced vulnerable code sections with secure alternatives based on embedding probabilities within a Few-Shot Learning (FSL) framework, achieving 89.9% accuracy using approximately 4000 samples. Transformers also proved effective for Java code analysis. SeqTrans [31] addressed vulnerabilities across 624 Common Vulnerabilities and Exposures (CVEs) related to incorrect authentication, critical resource misuse, and numeric errors. SeqTrans transformed Java code into token sequences using byte-pair encoding and applied a Neural Machine Translation (NMT)-style transformer to capture hidden representations. A Beam Search (BS) mechanism identified vulnerable sections for correction through syntax verification, achieving statement-level fix rates of 23.3% and CVE-level fixes of 25.3%. Expanding on SeqTrans, VRepair [32] introduced a transformer-based encoder-decoder architecture for repairing vulnerabilities in C code. Using a dataset of 655,741 committed vulnerabilities, the model achieved precision rates between 22.55% and 27.59%. VRepaircombined a multi-attention transformer with a Sequential Neural Network (SNN) to analyze and repair buffer overflows and resource management errors. Beam Search was used to generate patches while maintaining syntax and context integrity. Hybrid models have also been developed for vulnerability detection. DB-CBIL (DistilBERT-Based Transformer Hybrid Model Using CNN and BiLSTM) [33] integrated ASTs and DistilBERT with a Convolutional Neural Network (CNN) for feature extraction and a Bidirectional Long Short-Term Memory (BiLSTM) layer for sequential data processing. By segmenting C/C++ code into 1-gram tokens, DB-CBIL captured both syntactic and semantic features effectively. The model utilized a dataset of 33,360 functions (12,303 labeled as vulnerable and 21,057 as non-vulnerable), achieving a precision of 99.51%. Clustering-based approaches have also been explored. VuRLE (VuRLE: Automatic Vulnerability Detection and Repair by Learning from Examples) [34] applied ASTs and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to Java code.