Assurance 2.0 is a modern framework developed to address the assurance challenges of increasingly complex, adaptive, and autonomous systems. Building on the traditional Claims-Argument-Evidence (CAE) model, it introduces reusable assurance theories and explicit counterarguments (defeaters) to enhance rigor, transparency, and adaptability. It supports continuous, incremental assurance, enabling innovation without compromising safety. However, limitations persist in confidence measurement, residual doubt management, automation support, and the practical handling of defeaters and confirmation bias. This paper presents \textcolor{black}{a set of decomposition frameworks to identify a complete set of safety arguments and measure their corresponding evidence.} Grounded in the Assurance 2.0 paradigm, the framework is instantiated through a structured template and employs a three-tiered decomposition strategy. \textcolor{black}{A case study regarding the application of the decomposition framework in the end-to-end (E2E) AI-based Self-Driving Vehicle (SDV) development is also presented in this paper.} At the top level, the SDV development is divided into three critical phases: Requirements Engineering (RE), Verification and Validation (VnV), and Post-Deployment (PD). Each phase is further decomposed according to its Product Development Lifecycle (PDLC). To ensure comprehensive coverage, each PDLC is analyzed using an adapted 5M1E model (Man, Machine, Method, Material, Measurement, and Environment). Originally developed for manufacturing quality control, the 5M1E model is reinterpreted and contextually mapped to the assurance domain. This enables a multi-dimensional decomposition that supports fine-grained traceability of safety claims, evidence, and potential defeaters.

翻译：Assurance 2.0是为应对日益复杂、自适应和自主系统的保证挑战而开发的现代框架。它在传统的“声明-论据-证据”（CAE）模型基础上，引入了可重用的保证理论和显式的反驳论点（击败者），以增强严谨性、透明度和适应性。它支持持续、增量的保证，使得在不牺牲安全性的前提下进行创新成为可能。然而，该框架在置信度度量、残余疑虑管理、自动化支持以及实际处理击败者和确认偏误方面仍存在局限。本文提出了一套分解框架，用于识别完整的安全论证集合并度量其对应的证据。该框架基于Assurance 2.0范式，通过结构化模板进行实例化，并采用三层分解策略。本文还介绍了该分解框架在基于端到端（E2E）人工智能的自动驾驶汽车（SDV）开发中的应用案例研究。在顶层，SDV开发被划分为三个关键阶段：需求工程（RE）、验证与确认（VnV）以及部署后（PD）。每个阶段根据其产品开发生命周期（PDLC）进一步分解。为确保全面覆盖，每个PDLC均采用经过调整的5M1E模型（人、机、法、料、测、环）进行分析。5M1E模型最初为制造业质量控制而开发，本文对其进行了重新诠释，并将其上下文映射到保证领域。这实现了一种多维度的分解，支持对安全声明、证据和潜在击败者进行细粒度的可追溯性。