迈向自动化虚拟电子控制单元（ECU）双胞胎的软件测试
Toward Automated Virtual Electronic Control Unit (ECU) Twins for Shift-Left Automotive Software Testing
AI总结：
本研究提出一种自动化虚拟ECU双胞胎方法，通过代理驱动的工作流和反馈机制，实现早期软件测试和集成，提升测试的可重复性和安全性。
AI中文摘要：
汽车软件日益超越硬件可用性，迫使后期集成和昂贵的硬件在回路（HiL）瓶颈。InnoRegio挑战项目研究了一个虚拟测试和集成环境是否能够足够早地重现电子控制单元（ECU）行为，以便在物理硬件存在之前运行真实的软件二进制文件。我们报告了一个原型，该原型使用代理驱动的反馈工作流和参考模拟器通过GNU调试器（GDB）生成准确的处理器模型。结果表明，通过自动化差分测试和迭代模型修正，可以减少最关键的技术风险——CPU行为忠实度。我们总结了架构、代理建模循环和项目成果，并推断出与报告的定性发现一致的合理技术细节。尽管云规模部署和完整工具链集成仍需未来工作，该原型展示了虚拟ECU双胞胎可行的左移路径，使可重复测试、非侵入式跟踪和符合安全标准的故障注入活动成为可能。
英文摘要：
Automotive software increasingly outpaces hardware availability, forcing late integration and expensive hardware-in-the-loop (HiL) bottlenecks. The InnoRegioChallenge project investigated whether a virtual test and integration environment can reproduce electronic control unit (ECU) behavior early enough to run real software binaries before physical hardware exists. We report a prototype that generates instruction-accurate processor models in SystemC/TLM~2.0 using an agentic, feedback-driven workflow coupled to a reference simulator via the GNU Debugger (GDB). The results indicate that the most critical technical risk -- CPU behavioral fidelity -- can be reduced through automated differential testing and iterative model correction. We summarize the architecture, the agentic modeling loop, and project outcomes, and we extrapolate plausible technical details consistent with the reported qualitative findings. While cloud-scale deployment and full toolchain integration remain future work, the prototype demonstrates a viable shift-left path for virtual ECU twins, enabling reproducible tests, non-intrusive tracing, and fault-injection campaigns aligned with safety standards.
