On the Viability of Decision Trees for Learning Models of Systems

Abstract models of embedded systems are useful for various tasks, ranging from diagnosis, through testing to monitoring at run-time. However, deriving a model for an unknown system is difficult. Generic learners like decision trees can identify specific properties of systems and have been applied successfully, e.g., for anomaly detection and test case identification. We consider Decision Tree Learning (DTL) to derive a new type of model from given observations with bounded history for systems that have a Mealy machine representation. We prove theoretical limitations and evaluate the practical characteristics in an experimental validation.