Machine Learning-assisted Fault Injection - Position Papers SAFECOMP 2020 International Conference on Computer Safety, Reliability and Security Access content directly
Conference Papers Year : 2020

Machine Learning-assisted Fault Injection

Mehrdad Moradi
Bentley James Oakes
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Joachim Denil
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Fault Injection (FI) is a method for system validation and verification in which the tester evaluates the system behavior resulting from the introduction of faults into the system under test. This paper proposes a model-based approach to improve the efficiency of the FI process by utilizing Machine Learning (ML) and formalized domain knowledge. This ML algorithm uses a probabilistic automaton to reduce the manual effort required in the testing procedure as the algorithm can automatically make decisions and predictions about catastrophic fault parameters. This assists the tester in dealing with complicated and broad-scale systems by enabling higher fault coverage with fewer simulations.
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Dates and versions

hal-02931709 , version 1 (07-09-2020)
hal-02931709 , version 2 (15-09-2020)


  • HAL Id : hal-02931709 , version 2


Mehrdad Moradi, Bentley James Oakes, Joachim Denil. Machine Learning-assisted Fault Injection. 39th International Conference on Computer Safety, reliability and Security (SAFECOMP), Position Paper, Lisbon, Portugal, Sep 2020, Lisbon, Portugal. ⟨hal-02931709v2⟩


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