Log inference on the Ripple Protocol: testing the system with an empirical approach
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Abstract
Ripple is a relatively new payments network that aims to improve the financial system by unifying its underlying infrastructure. Given its critical function, its system must be reliable and free of bugs. Therefore it should be tested extensively. One of the test methods that has not been used on it yet is log inference, a method that has a good potential for modelling complex communication protocols. Therefore, we have developed an empirical model of the Ripple Consensus Protocol by learning a Deterministic Finite Automaton (DFA) from the log files of two servers in the Ripple network. We have also developed a theoretical DFA of the Ripple Consensus Protocol and compared this to the empirical model to verify that the two systems function comparably. There has been found one notable difference between the two models, but whether this difference has a critical impact remains to be discussed.