Present day autonomous systems are cyber-physical systems (CPS) that involve multi-dimensional interaction between the cyber machine and the operational environment. One of the key aspects of these autonomous systems is that interaction patterns change over the lifetime of usage.
Changes in CPS interaction patterns can be unintentional or intentional changes in the software configuration of a CPS controller. Unintentional changes can occur through mechanisms such as patching. Intentional changes can include software doping by adding a piece of software that alters the operation of the CPS to bypass testing standards and/or circumvent regulations.
Test case generation to identify such unintentional or intentional changes in the operational characteristics is an important topic of recent research. A primary challenge is that comprehensive testing and monitoring of the CPS can only be done before deployment through a certification mechanism. Once it is deployed, continuous testing and monitoring cannot be performed or is limited due to exorbitant cost and inconvenience. Even more specifically, challenges to generating input test cases for compliance testing in dynamical cyber-physical interactions in a CPS include a) Zeno behavior in zero crossing events, and b) divergence in solution based on initial conditions. Thus, there is a need for a method that generates test cases for compliance testing of CPS that tackle these issues.
Researchers at Arizona State University have developed a method for generating test cases for compliance testing of cyber-physical systems (CPS). The generation method is aware of the dynamical cyber-physical interactions in a CPS. The method can identify operational changes in a CPS and detect cheating during CPS certification.
Related Publication: CyPhyTest: Cyber Physical Interaction Aware Test Case Generation to Identify Operational Changes
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