## 2 Related Works  
In this section of the study, approaches that detect vulnerabilities through source code are examined. In source-code vulnerability analyses, language-based learning algorithms are generally focused on. [PERSON] et al. utilized deep learning methods to analyze source code based on a code feature graph. The study used the Software Assurance Reference Dataset (SARD), a publicly available dataset of C/C++ instruction injections. The femeasure, precision, false-positive rate, true-positive rate and false-negative rate of the proposed deep learning code property graph-based vulnerability analysis (CPGVA) method were calculated [13]. [PERSON] and [PERSON] used a real-world SA dataset based on three open-source PHP applicationsto detect vulnerabilities in open-source software programs and to detect malware. A new deep-learning-based vulnerability detection model was proposed to identify features [14]. [PERSON] and [PERSON] proposed a deep-learning-based automatic vulnerability analysis system (AutoVAS) that effectively represented source code as embedding vectors using datasets from various projects in the National Vulnerability Database (NVD) and Software Assurance Reference Database (SARD) [15]. In their study, [PERSON] et al. examined how machine learning techniques performed when predicting potentially vulnerable functions in JavaScript programs. For this research, they applied eight machine learning algorithms to build prediction models using a new dataset generated from vulnerability information in the public databases of the Node Security Project and the Snyk platform, as well as code fix patches on GitHub. They used static source-code metrics as predictors and an exhaustive grid-search algorithm to find the best performing models. The KNN machine learning algorithm showed the most successful results [16]. [PERSON] presented a machine learning classifier designed to detect SQL injection vulnerabilities in PHP code. Both classical and deep-learning-based machine learning algorithms were used to train and evaluate classifier models using input validation and sanitization features extracted from source-code files. In a ten-fold cross-validation, a model trained using a convolutional neural network (CNN) achieved the highest precision (95.4%), while a multilayer perceptron (MLP)-based model achieved the highest recall (63.7%) and the highest f-measure (0.746) [17]. [PERSON] et al. aimed to detect software vulnerabilities using a BERT-based model. As a method, vulnerabilities were classified more accurately by enabling the model to understand the syntactic and semantic aspects of the code. According to the numerical results obtained, the model performed with an F1-score of over 95% and high accuracy values. It was also reported that the model gave lower false-positive and -negative results compared with traditional methods. This reveals that BERT is an effective method to detect software vulnerabilities [18]. [PERSON] et al. focused on the detection of software vulnerabilities. The BBVD approach aims to automatically detect vulnerabilities using BERT-based models, treating high-level programming languages as natural languages. This approach provides more efficient results when traditional methods cannot effectively scan large amounts of source code. Experimental results showed that BBVD performed well in tests using SARD and Big-Vul datasets [19]. [PERSON] et al. introduced XGV-BERT, a deep-learning-based software vulnerability detection framework. XGV-BERT detected software vulnerabilities by combining a pre-trained CodeBERT model and graph neural network (GCN). The model learned the contextual relationships of source-code attributes by exploiting large datasets and using transfer learning. The results showed that XGV-BERT provided a higher accuracy than existing methods such as VulDeePecker and SySeVR, with an F1-score of 97.5% for VulDeePecker and 95.5% for SySeVR [20]. [PERSON] et al. aimed to detect types of software vulnerabilities.