A key challenge faced by small and medium-sized business entities is securely
managing software updates and changes. Specifically, with rapidly evolving
cybersecurity threats, changes/updates/patches to software systems are
necessary to stay ahead of emerging threats and are often mandated by
regulators or statutory authorities to counter these. However, security
patches/updates require stress testing before they can be released in the
production system. Stress testing in production environments is risky and poses
security threats. Large businesses usually have a non-production environment
where such changes can be made and tested before being released into
production. Smaller businesses do not have such facilities. In this work, we
show how "digital twins", especially for a mix of IT and IoT environments, can
be created on the cloud. These digital twins act as a non-production
environment where changes can be applied, and the system can be securely tested
before patch release. Additionally, the non-production digital twin can be used
to collect system data and run stress tests on the environment, both manually
and automatically. In this paper, we show how using a small sample of real
data/interactions, Generative Artificial Intelligence (AI) models can be used
to generate testing scenarios to check for points of failure.

通过在云端创建数字孪生，本文展示了如何在小规模企业中实现安全地进行软件更新与变更，利用数字孪生进行非生产环境的测试并采集系统数据进行压力测试，同时利用生成式人工智能模型生成测试场景以检测错误点。

数字孪生上的生成建模用于变革管理

Change Management using Generative Modeling on Digital Twins

Staged rollout is a strategy of incrementally releasing software updates to
portions of the user population in order to accelerate defect discovery without
incurring catastrophic outcomes such as system wide outages. Some past studies
have examined how to quantify and automate staged rollout, but stop short of
simultaneously considering multiple product or process metrics explicitly. This
paper demonstrates the potential to automate staged rollout with
multi-objective reinforcement learning in order to dynamically balance
stakeholder needs such as time to deliver new features and downtime incurred by
failures due to latent defects.

本文研究了如何自动化分阶段软件发布，以加速缺陷发现，并通过多目标强化学习来平衡利益相关者的需求，例如交付新功能的时间和因潜在缺陷而导致的停机时间。