轻胜于重：基于语言诱导的性能需求自动量化
Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement
AI总结：
本文提出轻量级语言诱导匹配方法LQPR，将性能需求量化转化为分类问题，在多个数据集上以低两个数量级的成本超越九种基于学习的方法，证明专用方法在性能需求量化中优于通用大语言模型方法。
摘要：
在配置调优和性能测试等不同工程任务中，为了满足合规性要求，所获取的性能需求需要被量化。现有大量工作依赖人工量化，由于不精确性，这种方式成本高昂且容易出错。本文提出了LQPR，一种高效的性能需求自动量化方法。LQPR基于一个新的理论框架，将量化问题转化为分类问题。尽管大语言模型（LLMs）在需求分析中应用广泛，LQPR采用了不同的视角来解决该分类问题：我们观察到性能需求往往表现出强模式且通常简短精炼，因此设计了一种轻量级的语言诱导匹配机制。我们在多个数据集上将LQPR与九种最先进的基于学习的方法进行了比较，结果表明LQPR在75%或更多的案例中被列为唯一最优方法，且成本低两个数量级。我们的工作证明，至少在性能需求量化方面，专用方法可能比通用的LLM驱动方法更为适用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
