Lunch will be served at 11:45 AM.
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.
Betty H.C. Cheng is a professor in the Department of Computer Science and Engineering at Michigan State University. She has also been the Industrial Relations Manager and senior researcher for BEACON, the National Science Foundation Science and Technology Center in the area of Evolution in Action. Her research interests include self-adaptive autonomous systems, safe use of AI-enabled systems, requirements engineering, model-driven engineering, automated software engineering, and harnessing evolutionary computation and search-based techniques to address software engineering problems. These research areas are used to support the development and maintenance 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. She has collaborated with Ford, General Motors, ZF, Motorola, and Siemens. Previously, she was awarded an NASA/JPL Faculty Fellowship to investigate the use of new software engineering techniques for a portion of the NASA space shuttle software. She currently has projects in the areas of assured autonomy (systems with machine learning components), model-driven approaches to autonomous systems and digital twins, 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, AFRL, ONR, DARPA, NASA, ARO, and numerous industrial organizations. She serves on the journal editorial boards for ACM Transactions for Autonomous and Adaptive Systems, as well as Software and Systems Modeling; she has served as Co-Associate Editor-in-Chief and two terms as an Associate Editor for IEEE Transactions for Software Engineering and Requirements Engineering Journal. 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 Bachelor of Science degree 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.