Addressing Assurance for Self-Adaptive Systems in the Face of Uncertainty
Betty H. C. Cheng · 2016
This presentation will overview several research projects that investigate novel ways to model, analyze, and mitigate uncertainty for self-adaptive cyber-physical systems, with a particular focus on assurance. First, uncertainty about the physical environment can lead to suboptimal, and sometimes catastrophic, results as the system tries to adapt to unanticipated or poorly-understood environmental conditions. Second, uncertainty in the cyber environment can lead to unexpected and adverse effects, including not only performance impacts (load, traffic, etc.) but also potential threats or overt attacks. Finally, uncertainty can exist with the components themselves and how they interact upon reconfiguration, including unexpected and unwanted feature interactions. Each of these sources of uncertainty can potentially be identified at different stages, respectively design time and run time, but their mitigation might be done at the same or at a different stage. Based on the related literature and our investigations, we argue that the following three overarching techniques are essential and warrant further research to provide enabling technologies to address uncertainty during both stages: model-based development, automated assurance techniques, and self-adaptation. Furthermore, we posit that in order to go beyond incremental improvements to current software engineering techniques, we need to infuse these three areas with successful techniques and inspirations from other disciplines, such as control theory, machine learning, and biology.