# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 3 Research Methodology  
This section outlines the research methodology employed in this study, which focuses on developing a comprehensive framework known as the C2B hybrid model for source code retrieval. In light of the growing complexity of software systems and the extensive repositories of available code, the efficient and precise retrieval of pertinent code snippets has become increasingly vital for software developers. To address this challenge, we harness the advancements in deep learning and natural language processing. Our methodology involves a unique fusion of two powerful models: CodeT5, which is known for its good code comprehension capabilities, and the bidirectional long short-term memory (Bi-LSTM) model, known for its good sequential modeling capabilities. This integration aims to elevate the performance of source code retrieval by effectively capturing both the semantic relationships and sequential dependencies inherent in natural language queries and code snippets. The research methodology encompasses several key steps, as illustrated in Figure 1, which depicts the workflow of the proposed C2B hybrid model. This figure visually guides our approach, allowing for a clear understanding of the steps involved in achieving our research objectives. The research methodology covers the following key steps:  
1. Data collection: Use the CodeSearchNet dataset for training and evaluating the C2B hybrid model. The dataset consists of pairs of natural language comments and corresponding code snippets along with relevant labels indicating the relationship between the query and code. 2. Data preprocessing: Perform preprocessing of the collected dataset, including tokenization and encoding of queries/comments and code snippets which are then feed into the C2B hybrid model. The queries/comments and code snippets are tokenized, breaking them down into individual words or tokens. Then, these tokens are encoded into numerical representations to be fed into the neural network models. A vocabulary is created by collecting all unique tokens from the dataset, enabling the mapping of tokens to their numerical representations during encoding. 3. C2B hybrid model architecture design: Develop a hybrid architecture that combines the strengths of CodeT5 and Bi-LSTM for source code retrieval. The architecture of the C2B hybrid model determines and exchanges information necessary for source code retrieval. The proposed architecture defines the specific layers, connections, and mechanisms for integrating the two models. 4. C2B hybrid model implementation: Implement the proposed C2B hybrid model architecture using a deep learning framework such as TensorFlow or PyTorch. Construct the necessary layers, connections, and mechanisms as defined in the model design phase. Incorporate appropriate attention mechanisms or other enhancements to improve the model's performance. * Fine-tuning and training: Perform the fine-tuning process for both CodeT5 and Bi-LSTM components of the hybrid model. Initialize the CodeT5 component with pre-trained weights from the pretraining phase, and train both components using the collected dataset. Determine the appropriate loss functions and optimization algorithms for training the hybrid model. * Evaluation metrics: Select suitable evaluation metrics for assessing the performance of the hybrid model. Common metrics for source code retrieval include precision, recall, mean average precision (MAP), and normalized discounted cumulative gain (NDCG). Determine the evaluation protocol, such as train-test splits or cross-validation, to measure the model's effectiveness. * Experimental evaluation: Conduct experiments to evaluate the hybrid CodeT5 and Bi-LSTM model on the collected dataset. Compare its performance against baseline models or existing state-of-the-art approaches. Perform statistical analysis to assess the significance of the results and identify areas of improvement.