Architectural self-organisation, in which different configurations of software modules are dynamically assembled based on the current context, has been shown to be an effective way for software to self-optimise over time. Current approaches to this rely heavily on human-led definitions: models, policies, and processes to control how self-organisation works. We present the case for a paradigm shift to fully emergent computer software that places the burden of understanding entirely into the hands of software itself. These systems are autonomously assembled at runtime from discovered constituent parts and their internal health and external deployment environment continually monitored. An online, unsupervised learning system then uses runtime adaptation to continuously explore alternative system assemblies and locate optimal solutions. Based on our experience over the past 3 years, we define the problem space of emergent software and present a working case study of an emergent web server as a concrete example of the paradigm. Our results demonstrate two main aspects of the problem space for this case study: that different assemblies of behaviour are optimal in different deployment environment conditions and that these assemblies can be autonomously learned from generalised perception data while the system is online.

中文翻译：

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使用持续自组装、感知和学习来定义新兴软件

架构自组织，其中软件模块的不同配置基于当前上下文动态组装，已被证明是软件随时间自我优化的有效方式。目前的方法在很大程度上依赖于人为主导的定义：模型、政策和流程来控制自组织的工作方式。我们提出了将范式转变为完全涌现将理解的负担完全交给软件本身的计算机软件。这些系统在运行时从发现的组成部分自主组装，并持续监控其内部健康和外部部署环境。然后，在线无监督学习系统使用运行时适应来不断探索替代系统组件并找到最佳解决方案。根据我们过去 3 年的经验，我们定义了紧急软件的问题空间，并提出了一个紧急 Web 服务器的工作案例研究，作为该范式的具体示例。我们的结果展示了本案例研究的问题空间的两个主要方面：