In recent years, the complexity of real-time embedded systems has increased dramatically. For those modern real-time systems, the limitations of original static Response Time Analysis (RTA) become more and more conspicuous. Most static analysis methods not only require much detailed system information, but also only target to some specific system model with non-realistic assumptions. As a result, those methods may produce overly pessimistic results, making them unsuitable to be applied on a complex industrial system. The best system model may be the system itself. Therefore, statistical RTA, which can produce probabilistic analysis results based on samples provided by real systems or simulators, may become more expedient. Statistical RTA usually requires more relaxed assumptions and less system information than static RTA. In this paper, we present an Extreme Value Theory (EVT) based method to compute Worst-Case Response Time (WCRT) targeting complex real-time systems. In the evaluation phase, we have applied this method to the calculation of worst-case transmission delays of messages over Controller Area Network (CAN), and some comparisons with static RTA are also provided. According to the experimental results, as the system complexity increases, our approach performs much more stable and less pessimistic.
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