当心不断发展的“智能”网络服务！保护AI优先组件的集成架构策略
Beware the evolving 'intelligent' web service! An integration architecture tactic to guard AI-first components
摘要：
智能服务通过简单的RESTful API端点为开发人员提供AI的强大功能，从而抽象出机器学习的许多复杂性。然而，这些智能服务中的大多数（如计算机视觉）都是随着时间的推移不断学习的。当抽象的“黑盒子”内部的东西隐藏起来并不断发展时，依赖于这些不断发展的服务的应用程序的健壮性就会出现问题。如果开发人员不采用依赖于智能服务的项目规划和构建方式，就会在项目规划和开发中产生重大差距和风险。因此，软件工程师如何才能最好地降低软件演化风险，从而确保他们自己的应用程序保持质量？我们的建议是一个架构策略，旨在提高智能服务依赖软件的鲁棒性。该策略涉及创建一个以智能服务为基准的特定于应用程序的基准数据集，从而减轻进化行为的变化。我们对该架构的实现进行了技术评估，展示了该策略如何使用由331个图像组成的数据集识别1，054个显著置信度演变的案例和2，461个响应标签集发生重大变化的案例，这些图像在发送到服务时会发生演变。
英文摘要：
Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intelligent services-such as computer vision-continually learn with time. When the internals within the abstracted 'black box' become hidden and evolve, pitfalls emerge in the robustness of applications that depend on these evolving services. Without adapting the way developers plan and construct projects reliant on intelligent services, significant gaps and risks result in both project planning and development. Therefore, how can software engineers best mitigate software evolution risk moving forward, thereby ensuring that their own applications maintain quality? Our proposal is an architectural tactic designed to improve intelligent service-dependent software robustness. The tactic involves creating an application-specific benchmark dataset baselined against an intelligent service, enabling evolutionary behaviour changes to be mitigated. A technical evaluation of our implementation of this architecture demonstrates how the tactic can identify 1,054 cases of substantial confidence evolution and 2,461 cases of substantial changes to response label sets using a dataset consisting of 331 images that evolve when sent to a service.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
