Extending Decision Maps for Sustainable Safety and Security in Self-Adaptive Systems

2026-07-13

AI Summary

Existing decision graph approaches struggle to effectively model safety and security requirements in adaptive systems. This work proposes an extended decision graph modeling language that, for the first time, incorporates a safety-event dimension within a sustainability-driven framework and enables unified, synergistic modeling of safety, security, and sustainability through multi-granular “safety modes.” The approach supports formal specification and divide-and-conquer fine-grained management of relevant scenarios across the entire system lifecycle. Experimental evaluation on an industrial collaboration use case demonstrates that the proposed extension more accurately captures complex safety and security scenarios, significantly enhancing the overall modeling capability for adaptive systems.

Abstract

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.

Problem

• Self-Adaptive Systems
• Sustainability
• Safety
• Security
• Decision Maps

Innovation

• Decision Maps
• Self-Adaptive Systems
• Sustainability
• Safety Modeling
• Security Modes