C2B: A Semantic Source Code Retrieval Model using CodeT5 and Bi-LSTM

Abstract

To enhance the software implementation process, developers frequently leverage preexisting code snippets by exploring an extensive codebase. Existing code search tools often rely on keyword- or syntactic-based methods and struggle to fully grasp the semantics and intent behind code snippets. In this paper, we propose a novel hybrid C2B model that combines CodeT5 and bidirectional long short-term memory (Bi-LSTM) for source code search and recommendation. Our proposed C2B hybrid model leverages CodeT5’s domain-specific pretraining and Bi-LSTM’s contextual understanding to improve code representation and capture sequential dependencies. As a proof-of-concept application, we implemented the proposed C2B hybrid model as a deep neural code search tool and empirically evaluated the model on the large-scale dataset of CodeSearchNet. The experimental findings showcase that our methodology proficiently retrieves pertinent code snippets and surpasses the performance of prior state-of-the-art techniques.