Conformance Testing for Finite State Machines Guided by Deep Neural Network

Abstract

This paper proposes a Finite State Machine (FSM) testing technique based on deep neural network (DNN). This technique verifies the correctness of an implementation FSM-B of a specification FSM-A. Using the back-propagation algorithm, a deep neural network is trained with the input–output patterns for a given set of transition functions that specify an FSM. Initially, for FSM-A, the input patterns and the corresponding output patterns (I/O pairs) are generated. Then most of the patterns are used to train the DNN. Once the training is over, the DNN is validated with the remaining I/O pairs (around 20%). The model can be used for verifying the correctness of FSM-B after training and validation of the DNN. Some inputs are applied to FSM-B and the generated output patterns are compared with the predicted values of the proposed DNN. The difference of accuracy percentages between FSM-A and FSM-B is recorded and zero difference between them indicates the fault-free condition of the implementation FSM-B. To check the effectiveness of the scheme, the output- and state-type faults are injected to derive mutant FSMs. Experimental results performed on the MCNC FSM benchmarks prove the efficacy of the proposed method. Only a few numbers of tests are needed to detect the presence of anomaly, if any. Hence, the test time reduces significantly — resulting in an average test time reduction of 85.67% compared to the conventional techniques. To the best of our knowledge, for the first time a DNN-driven testing scheme is being proposed.