一句话：提出融合软件工程、演化计算与机器学习以支持「模型宇宙」持续演化的研究愿景。
智能软件系统面临不确定、不可预测、实时且终身的演化，其运行故障会带来重大损失与安全危害，因此保障系统在不确定性下决策的可信性是首要挑战。作者提出，应在系统整个生命周期内持续监控、测试与精化系统及其运行环境，并借助数字孪生等技术保持同步，以反映系统最新状态。这类表示通常由基于先验知识的模型与机器学习模型共同构成，即所谓「模型宇宙」。论文的核心愿景是将软件工程、演化计算与机器学习的技术相结合，使模型宇宙随系统宇宙共同演化，从而支撑可信决策。本文为愿景性论文，未报告具体实验评估结果。
Uncertain, unpredictable, real-time, and lifelong evolution causes operational failures in intelligent software systems, leading to significant damages, safety and security hazards, and tragedies. To fully unleash such systems' potential and facilitate their wider adoption, ensuring the trustworthiness of their decision-making under uncertainty is the prime challenge. To overcome this challenge, an intelligent software system and its operating environment should be continuously monitored, tested, and refined during its lifetime operation. Existing technologies, such as digital twins, can enable continuous synchronisation with such systems to reflect their most up-to-date states. Such representations are often in the form of prior-knowledge-based and machine-learning models, together called ‘model universe’. In this paper, we present our vision of combining techniques from software engineering, evolutionary computation, and machine learning to support the model universe evolution.