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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.07927 |
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| _version_ | 1866909533596549120 |
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| author | Li, Xia Kim, Allen |
| author_facet | Li, Xia Kim, Allen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Study to Evaluate the Impact of LoRA Fine-tuning on the Performance of Non-functional Requirements Classification Li, Xia Kim, Allen Software Engineering 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. |
| title | A Study to Evaluate the Impact of LoRA Fine-tuning on the Performance of Non-functional Requirements Classification |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.07927 |