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Applying Model-Driven Requirements Engineering to Manage Uncertainty for High-Assurance Self-Adaptive Systems: Lessons Learned and Research Challenges

Applying Model-Driven Requirements Engineering to Manage Uncertainty for High-Assurance Self-Adaptive Systems: Lessons Learned and Research Challenges

Speaker: Betty H.C. Cheng – East Lansing, MI, United States
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing , Architecture, Embedded Systems and Electronics, Robotics , Security and Privacy , Software Engineering and Programming

Abstract

This presentation will overview several research projects that explore how model-driven requirements engineering can been used to model, analyze, and mitigate uncertainty arising in three different aspects of high-assurance autonomous systems. 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 and mitigated at design time and run time. Based on a number of collaborative projects involving industry applications, we share lessons learned and identify research challenges to applying model-driven requirements engineering to address uncertainty posed by the changing roles of humans, computers, and their collective ecosystem.

About this Lecture

Number of Slides: 60
Duration: ~65 minutes
Languages Available: English
Last Updated:

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