Methodologies for development of complex systems and models include external reviews by domain and technology experts. Among others, such reviews can uncover undocumented built-in assumptions that may be critical for correct and safe operation or constrain applicability. Since such assumptions may still escape human-centered processes like reviews, agile development, and risk analyses, here, we contribute toward making this process more methodical and automatable. We first present a blueprint for a taxonomy and formalization of the problem. We then show that a variety of digital artifacts of the system or model can be automatically checked against extensive reference knowledge. Since mimicking the breadth and depth of knowledge and skills of experts may appear unattainable, we illustrate the basic feasibility of automation with rudimentary experiments using OpenAI's ChatGPT. We believe that systematic handling of this aspect of system engineering can contribute significantly to the quality and safety of complex systems and models, and to the efficiency of development projects. We dedicate this work to Werner Damm, whose contributions to modeling and model-based development, in industry and academia, with a special focus on safety, helped establish a solid foundation to our discipline and to the work of many scientists and professionals, including, naturally, the approaches and techniques described here.

翻译：复杂系统与模型的开发方法论包含领域专家与技术专家的外部评审。这类评审能够揭示未文档化的隐含假设——这些假设可能对正确安全运行至关重要，或限制系统适用性。鉴于此类假设仍可能逃脱以人为中心的流程（如评审、敏捷开发及风险分析），本研究致力于使该流程更加系统化与可自动化。我们首先提出问题分类与形式化的蓝图框架，继而论证系统或模型中的各类数字工件可通过广泛参考知识进行自动校验。尽管模仿专家的知识广度与深度看似遥不可及，我们通过基于OpenAI ChatGPT的基础实验验证了自动化基本可行性。我们相信，对系统工程中这一环节的系统化处理，将显著提升复杂系统与模型的质量与安全性，并提高开发项目效率。谨以此文敬献给Werner Damm——他在工业界与学术界对建模与基于模型的开发（尤其侧重安全性）的贡献，为本学科及众多科研工作者（自然包括本文所述方法与技术）奠定了坚实基础。