Virtualized-Fault Injection Testing: A Machine Learning Approach

We introduce a new methodology for virtualized fault injection testing of safety critical embedded systems. This approach fully automates the key steps of test case generation, fault injection and verdict construction. We use machine learning to reverse engineer models of the system under test. We use model checking to generate test verdicts with respect to safety requirements formalised in temporal logic. We exemplify our approach by implementing a tool chain based on integrating the QEMU hardware emulator, the GNU debugger GDB and the LBTest requirements testing tool. This tool chain is then evaluated on two industrial safety critical applications from the automotive sector.