A GraphRAG-Based Dual-Path Structural Diffusion Retrieval Framework for Requirement-Code Traceability Link Recovery

Requirement-Code Traceability Link Recovery (RC-TLR) maps requirements to the code artifacts that implement them. The task remains difficult because requirements and source code use different vocabularies and expose different structural cues. Recent large language model (LLM) methods can improve trace-link classification, but the final decision still depends on which candidate artifacts reach the validator. We propose DSDR, a Graph Retrieval-Augmented Generation (GraphRAG)-based dual-path structural diffusion framework for zero-shot RC-TLR. DSDR uses pre-trained embedding models and LLMs without project-specific trace supervision. It builds three-layer heterogeneous graphs for requirements and code, links requirement entities to code identifiers through cross-modal semantic alignment, and ranks candidate classes with semantic-gated forward diffusion and backward verification. The same diffusion states are backtracked into evidence paths for LLM-based structural validation. On five benchmark datasets, retrieval-only DSDR raises recall over the strongest evaluated RAG-style retrieval baseline on all five tasks and gives the top retrieval-only result on four tasks in this comparison. With GPT-4o validation, DSDR yields a trace-link set with higher precision than the strongest evaluated LLM-based baseline in the same evaluation setup, while the recall–precision balance varies by project. The comparison suggests that structural evidence is useful for zero-shot RC-TLR retrieval and for precision-oriented validation.