The University of Oslo is closed. The PhD defence and trial lecture will therefore be fully digital and streamed directly using Zoom. The host of the session will moderate the technicalities while the chair of the defence will moderate the disputation.
Ex auditorio questions: the chair of the defence will invite the audience to ask ex auditorio questions either written or oral. This can be requested by clicking 'Participants -> Raise hand'.
Trial lecture
"AI in Software Engineering"
Main research findings
- Self-healing is becoming a critical feature of Cyber-Physical Systems (CPSs). By detecting faults and applying recovery adaptations at runtime, self-healing behaviors can help CPSs to maintain functional normal in the presence of faults. CPSs with the self-healing feature are named as Self-Healing CPSs (SH-CPSs). Besides recovery, SH-CPSs have to deal with various uncertainties, such as measurement errors from sensors and actuation deviations from actuators. To assess the dependability of SH-CPSs, it is necessary to test if SH-CPSs can still behave as expected under uncertainty. However, the autonomy of self-healing behaviors and the impact of uncertainties make it challenging to conduct such testing. To this end, an executable model-based testing approach is proposed in this thesis. In this approach, the expected behaviors of the SH-CPS under test are specified as an executable test model. By executing the SH-CPS together with the test model, sending them the same test inputs, and comparing their consequent states, we can dynamically test the system against its test model. To detect failures in the most effective manner, reinforcement learning algorithms have been applied with the new testing approach to learn the optimal testing policy. Evaluation results have demonstrated the effectiveness of the new testing approach.
Contact information to Department: Mozhdeh Sheibani Harat