面向AI原生大规模敏捷软件开发中的保证闭合
Towards Assurance Closure in AI-Native Large-Scale Agile Software Development
- Ericsson（爱立信）
- Blekinge Institute of Technology（布莱金厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对AI原生大规模敏捷软件开发中保证推理机器可操作的缺口，提出基于共享语义保证层的高层架构及六项对应能力，以实现保证闭合。
AI中文摘要：
《AI原生宣言》设想了大规模敏捷软件开发，其中人类日益主导意图、风险和异常，而智能体执行更多工程流程。实现这一最终状态需要的不只是更好的代码生成，还需要保证闭合，即系统能确定必须为真的内容、判定并获取合适证据、评判证据可信度、通过变更保持证据有效性，并利用所得不确定性限制智能体权限。现有工作已在形式化方法、测试、仿真、保证案例、数字孪生和运行时保证领域提供了许多必要机制。我们指出了使相关保证推理足够机器可操作的六个剩余缺口，提出了一个基于共享语义保证层构建、具备对应六项能力的高层架构，并提出了四个研究问题以将该架构转化为可靠的、人在回路的AI原生研发体系。
英文摘要：
The AI-Native Manifesto envisions large-scale agile software development in which humans increasingly govern intent, risk, and exceptions while agents execute more of the engineering process. Realizing that end-state requires more than better code generation: it requires assurance closure, meaning that the system can establish what must be true, determine and obtain appropriate evidence, judge the credibility of that evidence, preserve its validity through change, and use the resulting uncertainty to bound agent authority. Existing work already provides many of the necessary mechanisms across formal methods, testing, simulation, assurance cases, digital twins, and runtime assurance. We identify six residual gaps in making the surrounding assurance reasoning sufficiently machine-operable, propose a high-level architecture with six corresponding capabilities built on a shared semantic assurance layer, and formulate four research questions to turn that architecture into dependable, human-on-the-loop, AI-native R&D.
