Dual-graph Error Propagation Model (DEPM) is a stochastic framework developed in our lab that captures system properties relevant to error propagation processes such as control and data flow structures and reliability characteristics of single components. The DEPM helps to estimate the impact of a fault of a particular component on the overall system reliability. The probability of a system failure, Mean Time Between Failures and Mean Time to Repair, and the expected number of failures are the quantitative results of the DEPM-based analysis. In addition, the DEPM provides an access to the powerful techniques from Probabilistic Model Checking (PMC) that allow the evaluation of advanced, highly customizable, time-related reliability properties, e.g., “the probability of a system failure during certain time units after the occurrence of a certain combination of errors in certain system components”.
For this reason, the DEPM models are automatically transformed to discrete-time Markov chain (DTMC) models and are analyzed using the state of the art probabilistic model checker PRISM. However, the new promising model checker Storm has been recently released.
This paper presents the results of the efficiency comparison of these two model checkers based on the automatically generated set of the DTMC models that are typical for the error propagation analysis tasks. Several computation engines that are supported by both model checkers have been taking into account. The paper also gives the general description of the DEPM workflow with the focus on the PMC interface.