通过交互式检索增强方法，精准量化模糊的软件性能需求。
Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation
- 基于特定问题知识检索并推理用户偏好，逐步缩小认知负担。
- 仅5轮交互即实现最高40倍的量化精度提升。
- 适合需要高精度需求建模的软件工程团队使用。
由于软件性能需求以自然语言记录，将其转化为数学表达是软件工程的关键。然而，需求表述的模糊性和人类认知的不确定性导致理解存在高度歧义，使得自动化量化成为未解难题。本文提出IRAP方法，通过交互式检索增强偏好收集，将性能需求转化为数学函数。IRAP不同于以往方法，它利用特定问题知识进行检索与推理，指导与利益相关者的渐进式交互，显著降低认知负担。在四个真实数据集上，与10种先进方法对比实验表明，IRAP在所有情况下均表现更优，仅需五轮交互即实现最高40倍的性能提升。
原文摘要 · Abstract (English)
Since software performance requirements are documented in natural language, quantifying them into mathematical forms is essential for software engineering. Yet, the vagueness in performance requirements and uncertainty of human cognition have caused highly uncertain ambiguity in the interpretations, rendering their automated quantification an unaddressed and challenging problem. In this paper, we formalize the problem and propose IRAP, an approach that quantifies performance requirements into mathematical functions via interactive retrieval-augmented preference elicitation. IRAP differs from the others in that it explicitly derives from problem-specific knowledge to retrieve and reason the preferences, which also guides the progressive interaction with stakeholders, while reducing the cognitive overhead. Experiment results against 10 state-of-the-art methods on four real-world datasets demonstrate the superiority of IRAP on all cases with up to 40x improvements under as few as five rounds of interactions.
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