Autonomous systems are susceptible to unknown safety issues due to overlooked dependencies among components of the system and the entities that are part of its operating environment. The current safety analysis techniques aids in identifying known safety issues but not overlooked/unknown safety issues. To identify unknown safety issues due to problematic interactions between components, in our previous work, we proposed safety assessment for concurrent components (SACC). Despite being more effective than FMEA and goal modeling, SACC suffers from some limitations such as not considering environmental entities and their properties, and a manual process for identifying associated components for the collective analysis. For a complex system with a large number of components, such an analysis can result in overlooking safety issues. To address these limitations, in this paper, we propose an association-driven safety analysis (ADSA) approach, which is extended and built on SACC. The approach uses a property-relation (PR) table and modified association rule mining algorithm to identify components and environmental entities that need to be considered together to detect overlooked or unknown safety issues. We evaluated our approach using four robotic systems and compared with SACC and systems theoretic process analysis (STPA). Our results show that our proposed approach, in particular using behavioral dependencies, is effective at exposing unknown safety issues.

Abstract In cyber‐physical systems such as robots and automated vehicles that rely heavily on batteries, safety, and energy conservation can result conflicting requirements when not considered together. In systems engineering, the development begins at a concept phase where we have high‐level information of different components of the system. During the concept phase, we perform hazard analysis and risk assessment, define safety goals, and derive safety requirements. This means requirement engineering occurs at the end of the concept phase. However, energy conservation recommendations are not taken into consideration until the detailed design with specific hardware and software is known. Hence, it is possible to recommend energy conservation behaviors that can compromise system's safety. If we perform a trade‐off analysis between safety and energy conservation at a concept phase, we can propose various design alternatives and choose the best one that offers a safe and energy saving architecture. To achieve this goal, we propose an approach for identifying safety issues that can be caused by energy conservation recommendations. To evaluate the effectiveness of our approach, we performed an empirical study on four robotic systems. Our results show that we can find energy conservation recommendations that can compromise safety at a concept phase.

In recent years, there has been an increase in automation of safety critical systems such as self-driving cars, caretaking robots, or rescue drones. With increase in automation, the risk of systems behaving in an undesired manner has also risen. Most safety analysis approaches predominantly concentrate on identifying or assessing how the failure of a component can affect the behavior of a system based on the system's requirements. Yet, undesired behaviors can also occur when multiple components concurrently exert opposite effects on a shared resource. To identify safety-critical issues due to undesired concurrent component behaviors, we propose a property-driven approach called safety assessment for concurrent components (SACC). SACC uses a combinatorial technique that considers the requirements specification of a system, expressed as the states and properties of the system's components for identifying undesired combinations of component behaviors. To evaluate SACC, we performed a study using a requirements document on a caretaking robot. Our results show that SACC identified 38%-80% more undesired system behaviors when compared to the control techniques.

Off-nominal behaviors (ONBs) have been a major concern in the areas of embedded systems and safety-critical systems. To address ONB problems, some researchers have proposed model-based approaches that can expose ONBs by analyzing natural language requirements documents. While these approaches produced promising results, they require a lot of human effort and time. In this paper, to reduce human effort and time, we propose a combinatorial-based approach, Combinatorial Causal Component Model (Combi-CCM), which uses structured requirements patterns and combinations generated using the IPOG algorithm. We conducted an empirical study using several requirements documents to evaluate our approach, and our results indicate that the proposed approach can reduce human effort and time while maintaining the same ONB exposure ability obtained by the control techniques.