Classifying Non-Functional Requirements (NFRs) in software development life cycle is critical. Inspired by the theory of transfer learning, researchers apply powerful pre-trained models for NFR classification. However, full fine-tuning by updating all parameters of the pre-trained models is often impractical due to the huge number of parameters involved (e.g., 175 billion trainable parameters in GPT-3). In this paper, we apply Low-Rank Adaptation (LoRA) fine-tuning approach into NFR classification based on prompt-based learning to investigate its impact. The experiments show that LoRA can significantly reduce the execution cost (up to 68% reduction) without too much loss of effectiveness in classification (only 2%-3% decrease). The results show that LoRA can be practical in more complicated classification cases with larger dataset and pre-trained models.

翻译：在软件开发生命周期中，对非功能性需求（NFRs）进行分类至关重要。受迁移学习理论的启发，研究人员应用强大的预训练模型进行NFR分类。然而，由于涉及参数数量巨大（例如GPT-3包含1750亿可训练参数），通过更新预训练模型全部参数进行完整微调通常不切实际。本文基于提示学习将低秩适应（LoRA）微调方法应用于NFR分类，以探究其影响。实验表明，LoRA能显著降低执行成本（最高减少68%），且分类效果损失有限（仅下降2%-3%）。结果表明，在数据集和预训练模型规模更大的复杂分类场景中，LoRA具有实际应用价值。