Seung Yeob Shin

Cyber-physical Systems (CPSs) are often safety-critical and deployed in uncertain environments. Identifying scenarios where CPSs do not comply with requirements is fundamental but difficult due to the multidisciplinary nature of CPSs. We investigate the testing of control-based CPSs, where control and software engineers develop the software collaboratively. Control engineers make design assumptions during system development to leverage control theory and obtain guarantees on CPS behaviour. In the implemented system, however, such assumptions are not always satisfied, and their falsification can lead the loss of guarantees. We define stress testing of control-based CPSs as generating tests to falsify such design assumptions. We highlight different types of assumptions, focusing on the use of linearised physics models. To generate stress tests falsifying such assumptions, we leverage control theory to qualitatively characterise the input space of a control-based CPS. We propose a novel test parametrisation for control-based CPSs and use it with the input space characterisation to develop a stress testing approach. We evaluate our approach on three case study systems, including a drone, a continuous-current motor (in five configurations), and an aircraft. Our results show the effectiveness of the proposed testing approach in falsifying the design assumptions and highlighting the causes of assumption violations.

Weakly hard real-time systems can, to some degree, tolerate deadline misses, but their schedulability still needs to be analyzed to ensure their quality of service. Such analysis usually occurs at early design stages to provide implementation guidelines to engineers so they can make better design decisions. Estimating worst-case execution times (WCET) is a key input to schedulability analysis. However, early on during system design, estimating WCET values is challenging, and engineers usually determine them as plausible ranges based on their domain knowledge. Our approach aims at finding restricted, safe WCET sub-ranges given a set of ranges initially estimated by experts in the context of weakly hard real-time systems. To this end, we leverage (1) multi-objective search aiming at maximizing the violation of weakly hard constraints to find worst-case scheduling scenarios and (2) polynomial logistic regression to infer safe WCET ranges with a probabilistic interpretation. We evaluated our approach by applying it to an industrial system in the satellite domain and several realistic synthetic systems. The results indicate that our approach significantly outperforms a baseline relying on random search without learning and estimates safe WCET ranges with a high degree of confidence in practical time (< 23 h).