Automotive electronic control units (ECUs) are intricate systems with hundreds of individual functions, numerous software components, and multiple interdependent tasks. A prevalent structural pattern in these systems are so-called cause-effect chains. While significant research efforts have been dedicated to the temporal analysis and optimization of these chains, particularly minimizing data age and function response times, other crucial non-functional properties remain relatively underexplored. In particular, the safety integrity level (SIL) classification substantially influences the system design by determining task colocation strategies. Improper sharing of functions or interweaving tasks with different safety levels can compromise the integrity of critical functions. Additionally, AUTOSAR basic software (BSW) (e.g. OS, runtime environment, communication stacks, or diagnostics) introduces complexity that varies based on task characteristics and SIL categories. Furthermore, memory requirements present another critical challenge, given the diversity of memory architectures and SIL-specific dependencies that strongly constrain task allocations. This paper thoroughly characterizes a real-world automotive application, describing an automotive application based on SIL constraints, the impact of basic software, and memory requirements. In this context, the Driverator configuration framework is introduced for scalable system analysis.

翻译：[翻译后的摘要] 汽车电子控制单元是由数百个独立功能、众多软件组件及多个相互依赖任务构成的复杂系统。此类系统中普遍存在的结构模式是所谓的因果链。尽管已有大量研究工作致力于这些链的时序分析与优化（特别是最小化数据时效和功能响应时间），但其他关键的非功能属性仍相对未得到充分探索。尤其值得关注的是，安全完整性等级分类通过确定任务共置策略显著影响系统设计。不同安全等级的功能共享不当或任务交叉交织会危及关键功能的完整性。此外，AUTOSAR基础软件（例如操作系统、运行时环境、通信栈或诊断模块）会根据任务特性和安全完整性等级类别引入不同程度的复杂性。考虑到存储架构的多样性及严格约束任务分配的安全完整性等级相关依赖，内存需求构成了另一项关键挑战。本文对真实世界汽车应用进行了全面表征，描述了基于安全完整性等级约束的汽车应用、基础软件的影响及内存需求。在此背景下，引入了Driverator配置框架以实现可扩展的系统分析。