A framework to measure the robustness of programs in the unpredictable environment

TL;DR

This paper introduces a probabilistic framework to quantify and evaluate the robustness, adaptability, and reliability of programs interacting with unpredictable environments, especially in IoT systems.

Contribution

It presents a novel probabilistic approach and an inference-based algorithm to measure system robustness, adaptability, and reliability in dynamic, uncertain environments.

Findings

Framework effectively measures robustness in IoT systems.

Algorithm accurately evaluates system adaptability and reliability.

Case studies demonstrate practical applicability of the approach.

Abstract

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, is proposed to evaluate the proposed metric and the aforementioned properties. We use two case studies to the describe and evaluate the proposed approach.