Extending Decision Maps for Sustainable Safety and Security in Self-Adaptive Systems
Sustainability refers to a system’s ability to maintain its functionality and endure over time. Hence, sustainability is a highly desirable property of software systems, including Self-Adaptive Systems (SASs). SASs can change (adapt) their behavior at runtime to continue achieving their objectives despite external or internal impacts. SASs’ intended long-term system behavior can be expressed through a sustainability-driven visual modeling notation called Decision Maps (DMs). Although DMs have been proven helpful, they lack adequate modeling support for safety and security concerns. We address this limitation by extending the current notation for sustainability-driven modeling of SASs to better accommodate the unique characteristics of safety and security scenarios. First, we introduce an additional modeling dimension to account for safety incidents. Second, we adopt a fine-grained divide-and-conquer approach, modeling from distinct temporal security viewpoints (“security modes”) to address security. We employ the extended DM notation in a real-world use case scenario provided by our industry partner to assess its feasibility and suitability for practitioners. Our results indicate that our modeling notation helps capture security and safety scenarios more accurately and provides holistic support for the self-adaptation life cycle phases.
Tue 8 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
11:30 - 13:00 | Assurance, Safety and ResilienceMain Track at Aula Magna "Carmen Tura" Chair(s): Houssam Hajj Hassan Orange Innovation | ||
11:30 15mPaper | Self-adaptive Distributed Runtime Monitoring for Autonomous Systems Main Track Thomas Wright , Morten Haahr Kristensen Aarhus University, Mirgita Frasheri Aarhus University, Peter Larsen , Lukas Esterle Aarhus University, Claudio Gomes Aarhus University, Denmark | ||
11:45 15mPaper | Who Is Responsible? Self-Adaptation Under Multiple Concurrent Failures With Unknown Faults in Complex Robotic Systems Main Track Andreas Wiedholz XITASO GmbH IT & Software Solutions, Rafael Paintner German Aerospace Center Institute of Flight Systems, Alwin Hoffmann Universität Augsburg, Tobias Huber Technische Hochschule Ingolstadt | ||
12:00 15mPaper | When Should a Robot Stop Learning and Start Reasoning? Main Track Mahsa Nikmard Gran Sasso Science Institute (GSSI), Patrizio Pelliccione Gran Sasso Science Institute, L'Aquila, Italy, Gianlorenzo D'Angelo Gran Sasso Science Institute (GSSI) | ||
12:15 10mShort-paper | Extending Decision Maps for Sustainable Safety and Security in Self-Adaptive Systems Main Track Marco Stadler Johannes Kepler University Linz, Wesley K.G. Assunção North Carolina State University, Michael Vierhauser University of Innsbruck, Iris Groher Johannes Kepler University, Linz, Michael Riegler Johannes Kepler University Linz, Johannes Sametinger Pre-print | ||
12:25 10mShort-paper | Approved Too Late: Verdict Staleness in LLM-Guarded Self-Adaptive Systems Main Track Ilai Shraga Massachusetts Institute of Technology (MIT), Roei Eshel Maccabim-Re'ut High School, Lior Gorelik The Open University of Israel | ||
12:35 25mLive Q&A | Q&A and Panel Discussion Main Track | ||