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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.

ISO and IEC have jointly developed two Formal Description Techniques (FDTs) for specifying distributed real time systems such as computer/telecommunications protocols. These are Lotos and Estelle. In this paper, a formal method for automated transformation of a Lotos specification to an Estelle specification is presented. The method is applicable to various Lotos specification styles and to various communications protocols of ISO OSI layers.

Our method has applications in conformance testing of such systems and building common semantic model for the various FDTs. In this paper, we develop an algorithm for constructing a 'Data Oriented'-Restricted Behavior Tree T that represent both the control flow aspects and the data flow aspects of the system. Then, we develop an algorithm for constructing the Estelle specifications from T. A minimization rule is also developed to optimize the size of the Estelle specification by reducing both the number of states and the number of transitions.

Exchanging simulation models is currently of utmost importance. To improve interoperability between suppliers and original equipment manufacturers (OEMs), the functional mock-up interface (FMI) is exchanged in a standard format called functional mock-up unit (FMU). Since its first release, many simulation tools took the initiative to support FMI. However, since then, there have been many complaints stating that exchanging models via FMI does not work as stable as expected. The reason usually turned out to be the implementation of tool vendors that sometimes fail to comply with the standard fully. This paper introduces a methodology for testing FMI compliance of importing simulation tools using a set of reference FMUs. The standard defines the implementation of FMI functions calling sequence in a state machine. Therefore, conformance testing (also called fault detection) from automata theory is utilized to produce reference FMUs based on the FMI state-machine.