Sungmin Kim  EunJin Jeong  Soonhoi Ha

10.5626/JOK.2025.52.10.813

automotive application non-deterministic event-driven task model worst-case execution scenario memory requirement analysis worst-case response time

The rise of Software-Defined Vehicle (SDV) and the adoption of centralized electronic/ electrical (E/E) architectures have accelerated the integration of Electronic Control Units (ECUs), resulting in increased communication complexity and data throughput in automotive applications. In such environments, the dynamic nature of event-driven task models complicates the prediction of response times and memory requirements. This paper presents a conservative yet realistic methodology for analyzing worst-case scenarios. It combines system profiling with Envelope Arrival Curves, superset-based event path grouping, and statistical event distribution techniques. By utilizing profiling logs collected from an actual automotive embedded system, the proposed method constructs worst-case execution scenarios and evaluates both Worst-Case Response Time (WCRT) and memory requirements through simulation. Experimental results demonstrate the impact of profiling log composition and task priority adjustments on system performance.