Re-Distill: A Multi-Stage Retrieval Framework for Functional–Non-Functional Requirement Linking in Software Engineering

Non-functional requirements (NFRs) are critical for ensuring software quality, yet they remain difficult to identify due to their implicit and loosely defined relationship with functional requirements (FRs). Existing research predominantly focuses on NFR classification, leaving the more practical problem of linking FRs with their corresponding NFRs largely underexplored. To bridge this gap, this research introduces Re-Distill, a framework that treats FR–NFR association as a retrieval task. It adopts a curriculum-guided, data-centric distillation strategy to improve semantic representations and capture the interdependencies between FRs and NFRs. The framework combines general semantic adaptation, domain-specific specialization, and teacher-guided hard-negative mining in a contrastive learning setting. During inference, it integrates dense and lexical retrieval with cross-encoder reranking to produce ranked NFR candidates for unseen FR queries. Experiments on an expanded FR–NFR dataset show consistent improvements throughout all training stages. The resulting model achieves a Recall@10 of 70.79%, significantly outperforming the zero-shot baseline (42.36% Recall@10). These results highlight the effectiveness of retrieval-based approaches for functional–non-functional requirement linking, providing a practical and scalable way to undertake software requirement analysis.