Due to the diffusion of IoT, modern software systems are often thought to control and coordinate smart devices in order to manage assets and resources, and to guarantee efficient behaviours. For this class of systems, which interact extensively with humans and with their environment, it is thus crucial to guarantee their correct behaviour in order to avoid unexpected and possibly dangerous situations. In this paper we will present a framework that allows us to measure the robustness of systems. This is the ability of a program to tolerate changes in the environmental conditions and preserving the original behaviour. In the proposed framework, the interaction of a program with its environment is represented as a sequence of random variables describing how both evolve in time. For this reason, the considered measures will be defined among probability distributions of observed data. The proposed framework will be then used to define the notions of adaptability and reliability. The former indicates the ability of a program to absorb perturbation on environmental conditions after a given amount of time. The latter expresses the ability of a program to maintain its intended behaviour (up-to some reasonable tolerance) despite the presence of perturbations in the environment. Moreover, an algorithm, based on statistical inference, it proposed to evaluate the proposed metric and the aforementioned properties. Throughout the paper, two case studies are used to the describe and evaluate the proposed approach.

翻译：摘 要：由于物联网的普及，现代软件系统通常用于控制和协调智能设备，以管理资产和资源，并保证高效行为。对于这类与人类及其环境广泛交互的系统而言，确保其正确行为至关重要，以避免意外甚至危险情况的发生。本文提出了一种衡量系统鲁棒性的框架，该能力指程序在环境条件变化时保持原始行为的容忍性。在该框架中，程序与环境的交互被表示为描述两者随时间演化的随机变量序列，因此所考虑的度量将基于观测数据概率分布进行定义。随后，该框架被用于定义适应性和可靠性概念：前者指程序在特定时间后吸收环境条件扰动的能力；后者指程序在环境扰动存在下维持其预期行为（在合理容忍范围内）的能力。此外，本文提出了一种基于统计推断的算法来评估所提出的度量及上述属性。文中通过两个案例研究对所提方法进行了描述与评估。