Taxonomy-Guided Reasoning for Requirements Classification: A Study in Aerospace Industry
Yixing Luo, Yang Liu, Xiaofeng Li, Xiaogang Dong, Bin Gu, Zhi Gang Jin, Mengfei Yang · 2025
Requirements classification, which organizes software requirements into structured categories, is crucial in safety-critical domains such as aerospace. However, practical implementation is challenging due to the absence of unified, domain-specific taxonomies, as different developers often adopt divergent classification schemes. Moreover, safety-critical requirements frequently intertwine functional and reliability constraints, creating complex multi-label classification challenges. Existing supervised learning approaches depend on large annotated datasets, which are rarely feasible in specialized industries, while current LLM-based methods face difficulties handling hierarchical, multi-label scenarios effectively. To address these issues, we propose TRClass, a novel taxonomy-guided classification approach. The key idea behind TRClass is to integrate domain knowledge into the classification process by first constructing a unified taxonomy semi-automatically, extracting structure from existing documents, and refining it with expert validation. TRClass then guides an LLM to classify requirements by reasoning step-by-step through the taxonomy hierarchy, using few-shot retrieval and confidence-based exploration to achieve accurate multi-label decisions. We validate TRClass using aerospace software requirements as a representative case study for safety-critical industries. Results show that TRClass consistently outperforms baselines, with all components contributing to its overall effectiveness, and remains robust across different LLM configurations. A user study further confirms its practical usability in real-world industrial scenarios.