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：由于物联网的普及，现代软件系统通常负责控制和协调智能设备，以管理资产和资源并确保高效运行。对于这类与人类及其环境广泛交互的系统，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。本文提出了一种框架，用于测量系统的鲁棒性，即程序在环境条件变化时保持原始行为的能力。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，所考虑的度量将在观测数据的概率分布之间定义。该框架随后被用于定义“适应性”和“可靠性”的概念。前者指程序在给定时间后吸收环境条件扰动的能力，后者指程序在环境存在扰动的情况下保持预期行为（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，用于评估所提出的度量及上述特性。全文通过两个案例研究来描述和评估所提出的方法。