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Evaluating robustness of hierarchical and multi-label requirements classification: Effects of data partitioning and supervision design
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DOI：10.1016/j.infsof.2026.108212.png)
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Abstract
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Natural language processing techniques for software requirements classification have achieved strong performance in binary Functional vs. Non-Functional Requirement (FR vs. NFR) tasks. However, performance degrades substantially when addressing deeper taxonomies, multi-label requirements, and evaluation protocols that better reflect real-world conditions. Existing studies largely rely on single datasets, flat or shallow taxonomies, and random cross-validation, so the effects of data partitioning and supervision design are insufficiently understood.
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