Diagnostic Coverage Estimation for Automotive SoCs Based on Colored Stochastic Petri Nets

Safety-critical systems can cause catastrophic effects when particular failures occur during their operation. These systems, used in diverse domains, including automotive SoCs, incorporate Safety Mechanisms (SMs) to enhance their safety performance and meet certification standards (e.g., ISO26262). A critical measure of safety performance is the Diagnostic Coverage (DC) of SMs, determined through extensive and expensive Gate-Level Fault Injection (FI) campaigns, as recommended by ISO26262. To address this challenge, designers need early DC estimation methods to efficiently develop more reliable SMs by reducing simulation times, redesign stages, and computational resources. Our previous work proposed an interactive simulation framework based on Colored Generalized Stochastic Petri Nets (CGSPN) for Fault Coverage (FC) estimation. This work incorporates the requirements of automotive safety standards to predict the efficiency of SMs. We propose a methodology for early-stage estimation of the DC, enabling efficient SM development and its Design Space Exploration (DSE), and the discovery of Failure Modes through CGSPN simulations. To the best of our knowledge, the proposed work is the first DC estimation approach based on high-level models such as CGSPN. The methodology was verified in an automotive SoC, showing an average estimation accuracy of 97.2% and a 175x speed-up for a Software Test Library (STL) compared to results obtained through an exhaustive RTL FI campaign.