Fragility-oriented testing with model execution and reinforcement learning
Self-healing is becoming an essential behavior of smart Cyber-Physical Systems (CPSs), which enables them to recover from faults by themselves. Such behaviors make decisions autonomously at runtime and they often operate in an uncertain physical environment making testing even more challenging. To this end, we propose Fragility-Oriented Testing (FOT), which relies on model execution and reinforcement learning to cost-effectively test self-healing behaviors of CPSs in the presence of environmental uncertainty. We evaluated FOT’s performance by comparing it with a Coverage-Oriented Testing (COT) algorithm. Evaluation results show that FOT significantly outperformed COT for testing nine self-healing behaviors implemented in three case studies. On average, FOT managed to find 80% more faults than COT and for cases when both FOT and COT found the same faults, FOT took on average 50% less time than COT.
|Keywords||Cyber-Physical Systems, Model execution, Reinforcement learning, Self-healing behaviors, Uncertainty|
|Series||Lecture Notes in Computer Science|
Ma, T. (Tao), Ali, S. (Shaukat), Yue, T. (Tao), & Elaasar, M. (2017). Fragility-oriented testing with model execution and reinforcement learning. In Lecture Notes in Computer Science. doi:10.1007/978-3-319-67549-7_1