Novel Application of Deep Learning for Adaptive Testing Based on Long Short-Term Memory | IEEE Conference Publication | IEEE Xplore

Novel Application of Deep Learning for Adaptive Testing Based on Long Short-Term Memory


Abstract:

Adaptive testing is a promising approach that practically ensures cost reduction and reliability for test strategy. In adaptive testing, the test content or pass/fail lim...Show More

Abstract:

Adaptive testing is a promising approach that practically ensures cost reduction and reliability for test strategy. In adaptive testing, the test content or pass/fail limits are not fixed as in conventional test, but depend on other test results of the currently or historically tested data. Based on recent progress in machine learning, a new Long Short-Term Memory (LSTM) which is more advanced than simple Recurrent Neuron Network (RNN) is proposed for defect screening. The simulation results have been compared with the other deep learning and traditional methods when patterns are increased and decreased. The comparisons show that the proposed RNN-based LSTM method has achieved remarkable improvements, i.e. 4.3% accuracy improvement and 2.32s time reduction during the test process.
Date of Conference: 23-25 April 2019
Date Added to IEEE Xplore: 11 July 2019
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Conference Location: Monterey, CA, USA

References

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