用语言模式自动量化性能需求，效率远超大模型方法。
Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement
- 将量化问题转为分类任务，设计轻量级语言匹配机制。
- 在多数据集上表现最优，成本仅为其他方法的1/100。
- 适合需要高效精准性能需求转化的工程场景。
性能需求需量化以支持配置调优与性能测试等工程任务。现有方法多依赖人工量化，成本高且易出错。本文提出LQPR，一种高效的自动化性能需求量化方法。LQPR基于新理论框架，将量化问题转化为分类任务，并利用性能需求短而具规律的特点，设计轻量级语言诱导匹配机制。在多个数据集上对比九种先进学习方法，结果显示其在75%以上情况下表现最优，且计算成本低两个数量级。研究证明，针对性能需求量化的专用方法可能优于通用的大语言模型方案。
原文摘要 · Abstract (English)
Elicited performance requirements need to be quantified for compliance in different engineering tasks, e.g., configuration tuning and performance testing. Much existing work has relied on manual quantification, which is expensive and error-prone due to the imprecision. In this paper, we present LQPR, a highly efficient automatic approach for performance requirements quantification.LQPR relies on a new theoretical framework that converts quantification as a classification problem. Despite the prevalent applications of Large Language Models (LLMs) for requirement analytics, LQPR takes a different perspective to address the classification: we observed that performance requirements can exhibit strong patterns and are often short/concise, therefore we design a lightweight linguistically induced matching mechanism. We compare LQPR against nine state-of-the-art learning-based approaches over diverse datasets, demonstrating that it is ranked as the sole best for 75% or more cases with two orders less cost. Our work proves that, at least for performance requirement quantification, specialized methods can be more suitable than the general LLM-driven approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。