{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:21:34Z","timestamp":1774369294730,"version":"3.50.1"},"reference-count":33,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p> 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\u2013output 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\u00a0\u2014 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. <\/jats:p>","DOI":"10.1142\/s0218126622501560","type":"journal-article","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T11:36:49Z","timestamp":1645184209000},"source":"Crossref","is-referenced-by-count":1,"title":["Conformance Testing for Finite State Machines Guided by Deep Neural Network"],"prefix":"10.1142","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1383-1543","authenticated-orcid":false,"given":"Habibur","family":"Rahaman","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India"}]},{"given":"Santanu","family":"Chattopadhyay","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India"}]},{"given":"Indranil","family":"Sengupta","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India"}]}],"member":"219","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"S0218126622501560BIB001","volume-title":"Fault Detection in Digital Circuits","author":"Friedman A. 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