PseudoBridge: Pseudo Code as the Bridge for Better Semantic and Logic Alignment in Code Retrieval

Code retrieval aims to precisely find relevant code snippets that match natural language queries within massive codebases, playing a vital role in software development. Recent advances leverage pre-trained language models (PLMs) to bridge the semantic gap between unstructured natural language (NL) and structured programming languages (PL), yielding significant improvements over traditional information retrieval and early deep learning approaches. However, existing PLM-based methods still encounter key challenges, including a fundamental semantic gap between human intent and machine execution logic, as well as limited robustness to diverse code styles. To address these issues, we propose PseudoBridge , a novel code retrieval framework that introduces pseudo-code as an intermediate, semi-structured modality to better align NL semantics with PL logic. Specifically, PseudoBridge consists of two stages: First, we employ an advanced large language model (LLM) to synthesize pseudo-code, enabling explicit alignment between NL queries and pseudo-code. Second, we introduce a logic-invariant code style augmentation strategy and employ the LLM to generate stylistically diverse yet logically equivalent code implementations with pseudo-code, then align the code snippets of different styles with pseudo-code, enhancing model robustness to code style variation. We build PseudoBridge across 10 different PLMs and evaluate it on 6 mainstream programming languages. Extensive experiments demonstrate that PseudoBridge consistently outperforms baselines, achieving significant improvements in generalization, particularly in zero-shot scenarios like Solidity and XLCoST. Extended evaluations using open-source LLMs and advanced embeddings confirm that these gains stem from PseudoBridge's intrinsic design, independent of specific closed-source models. PseudoBridge achieves performance comparable to state-of-the-art embedding methods, highlighting the effectiveness of explicit logical and semantic alignment via pseudo-code as a robust solution for code retrieval.