On Freedom from Interference in Mixed-Criticality Systems: A Causal Learning Approach

摘要

Freedom from Interference (FFI) is one of the critical criteria to support coexistence of elements with different criticality in a mixed-criticality system (MCS). The principle of FFI is to ensure an element with lower criticality cannot influence an element with higher criticality. Existing FFI analysis solutions mainly focus on product development phase, and most are qualitative methods. They were inefficient to provide a comprehensive coverage of trouble spots due to wide environmental or usage model variations in many applications. They were also difficult to differentiate cascading effect from common-source effect. In this paper we propose a causal learning method based upon mining in-field anomaly data, which have embodied rich real-life information, to verify FFI. It is expected to address the aforementioned challenges, providing a new way to identify interference issues.