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An input to a system reveals a non-robust behaviour when, by making a smallchange in the input, the output of the system changes from acceptable (passing)to unacceptable (failing) or vice versa. Identifying inputs that lead tonon-robust behaviours is important for many types of systems, e.g.,cyber-physical and network systems, whose inputs are prone to perturbations. Inthis paper, we propose an approach that combines simulation-based testing withregression tree models to generate value ranges for inputs in response to whicha system is likely to exhibit non-robust behaviours. We apply our approach to anetwork traffic-shaping system (NTSS) -- a novel case study from the networkdomain. In this case study, developed and conducted in collaboration with anetwork solutions provider, RabbitRun Technologies, input ranges that lead tonon-robustness are of interest as a way to identify and mitigate networkquality-of-service issues. We demonstrate that our approach accuratelycharacterizes non-robust test inputs of NTSS by achieving a precision of 84%and a recall of 100%, significantly outperforming a standard baseline. Inaddition, we show that there is no statistically significant difference betweenthe results obtained from our simulated testbed and a hardware testbed withidentical configurations. Finally we describe lessons learned from ourindustrial collaboration, offering insights about how simulation helps discoverunknown and undocumented behaviours as well as a new perspective on usingnon-robustness as a measure for system re-configuration.

Learning Non-robustness using Simulation-based Testing: a Network Traffic-shaping Case Study