ChaT: Evaluation of Reconfigurable Distributed Network Systems Using Metamorphic Testing

Detecting faults in distributed network systems is challenging because of their complexity, but this is required to evaluate and improve their reliability. This paper proposes ChaT, a testing and evaluation methodology under system reconfigurations and perturbations for distributed network systems, to evaluate QoS reliability by discriminating safe and failure-prone behaviors from different testing scenarios. Motivated by meta-morphic testing technique that removes the burden of defining software oracles, we propose some metamorphic relationships that correlate system inputs and outputs to find patterns in executions. Classification techniques based on machine learning (principal component analysis and support vector machine) are used to identify system states and validate the proposed metamorphic relationships. These metamorphic relationships are also used to help anomaly detection. We verify this with several anomaly detection techniques (isolation forest, one-class SVM, local outlier factor, and robust covariance) that categorize experiments belonging to either safe or failure-prone states. We apply ChaT to a video streaming application use case. The simulation results show the effectiveness of ChaT to achieve our goals: identifying execution classes and detecting failure-prone experiments based on metamorphic relationships with high level of statistical scores.