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.

翻译：由于软件性能需求以自然语言形式记录，将其量化为数学形式对软件工程至关重要。然而，性能需求的模糊性与人类认知的不确定性导致需求解读存在高度模糊歧义，使得自动化量化成为尚未解决的挑战性问题。本文对该问题进行形式化定义，并提出IRAP方法——通过交互式检索增强偏好引致，将性能需求量化为数学函数。IRAP的独特之处在于，它显式地从问题特定知识出发进行偏好检索与推理，同时引导与利益相关者的渐进式交互，并降低认知负担。在四个真实数据集上与10种先进方法的实验结果表明，IRAP在所有案例中均展现出优越性，在仅需五轮交互的条件下性能提升高达40倍。