一句话：提出用场景理论为自适应软件的性能测试提供概率保证，并实证展示数据量与置信度的权衡及策略比较。
自适应软件系统的适应层性能测试因不确定性和变异性而十分困难，且难以给出形式化保证。本文提出一种严格的概率方法，为软件性能提供概率保证。文章给出应用该方法所需的实验设置，分析传统统计工具的局限，并引入场景理论（scenario theory）以克服这些限制。作者在 Tele-Assistance Service 和 Self-Adaptive Video Encoder 两个自适应应用上进行了系统实证评估：前者揭示数据收集量与测试置信度之间的权衡，后者展示如何比较不同适应策略。
This paper discusses the problem of testing the performance of the adaptation layer in a self-adaptive system. The problem is notoriously hard, due to the high degree of uncertainty and variability inherent in an adaptive software application. In particular, providing any type of formal guarantee for this problem is extremely difficult. In this paper we propose the use of a rigorous probabilistic approach to overcome the mentioned difficulties and provide probabilistic guarantees on the software performance. We describe the set up needed for the application of a probabilistic approach. We then discuss the traditional tools from statistics that could be applied to analyse the results, highlighting their limitations and motivating why they are unsuitable for the given problem. We propose the use of a novel tool – the scenario theory – to overcome said limitations. We conclude the paper with a thorough empirical evaluation of the proposed approach, using two adaptive software applications: the Tele-Assistance Service and the Self-Adaptive Video Encoder. With the first, we empirically expose the trade-off between data collection and confidence in the testing campaign. With the second, we demonstrate how to compare different adaptation strategies.