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A Multi-Disciplinary Approach to Addressing Uncertainty for High-Assurance Autonomous Systems


11/12/2020 at 3:55PM


11/12/2020 at 5:10PM


(This seminar is a virtual talk)


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Jane Cleland-Huang

Jane Cleland-Huang

VIEW FULL PROFILE Email: janeclelandhuang@nd.edu
Phone: 574-631-3637
Website: http://sarec.nd.edu/pages/ClelandHuang.html
Office: 354 Fitzpatrick
Software requirements traceability, software architecture, and the application of machine learning and data mining methods to address large-scale software and systems engineering problems.
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This presentation will overview several research projects that investigate novel ways to model, analyze, and mitigate uncertainty arising in three different aspects of cyber-physical 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 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, including biology and machine learning. 

Seminar Speaker:

Betty H.C. Cheng

Betty H.C. Cheng

Michigan State University

Betty H.C. Cheng is a professor in the Department of Computer Science and Engineering at Michigan State University. Her research interests include self-adaptive systems, requirements engineering, model-driven engineering, automated software engineering, and harnessing evolutionary computation to address software engineering problems. These research areas are used to support the development of high-assurance adaptive systems that must continuously deliver acceptable behavior, even in the face of environmental and system uncertainty. Example applications include intelligent transportation and vehicle systems. She collaborates extensively with industrial partners in her research projects in order to ensure real-world relevance of her research and to facilitate technology exchange between academia and industry. Previously, she was awarded a NASA/JPL Faculty Fellowship to investigate the use of new software engineering techniques for a portion of the shuttle software. She works extensively with industrial collaborators, including one sabbatical working with the Motorola Software Labs investigating automated analysis techniques of specifications of telecommunication systems. She was awarded an international faculty scholarship to explore research techniques for specifying and managing uncertainty in high-assurance systems. She is currently on sabbatical, where she is launching new projects in the area of model-driven approaches to sustainability, cyber security for automotive systems, and feature interaction detection and mitigation for autonomic systems, all in the context of operating under uncertainty while maintaining assurance objectives. Her research has been funded by several federal funding agencies, including NSF, ONR, DARPA, NASA, AFRL, ARO, and numerous industrial organizations. She is an Associate Editor-in-Chief for IEEE Transactions on Software Engineering, having previously served two terms on the editorial board. She also serves on the editorial boards for Requirements Engineering Journal and Software and Systems Modeling. She was the Technical Program Co-Chair for IEEE International Conference on Software Engineering (ICSE-2013), the premier and flagship conference for software engineering.

She received her BS from Northwestern University and her MS and PhD from the University of Illinois-Urbana Champaign, all in computer science. 

She may be reached at the Department of Computer Science and Engineering, Michigan State University, 3115 Engineering Building, 428 S. Shaw Lane, East Lansing, MI 48824; chengb@msu.edu; www.cse.msu.edu/~chengb.