一句话：提出基于应用专属基准数据集的架构战术，缓解依赖演化式智能服务的软件稳健性风险。
智能服务通过 RESTful API 为开发者提供 AI 能力，但计算机视觉等服务会持续学习演化，其黑箱内部变化会削弱依赖应用的稳健性。论文指出，若开发者不改变项目规划与构建方式，将在规划与开发中产生显著缺口与风险。为此，作者提出一种架构战术：构建面向具体应用、以目标智能服务为基线的基准数据集，通过持续比对识别并缓解服务演化带来的行为变化。技术评估在原型上使用 331 张图像组成的数据集调用服务，检测到 1,054 处置信度显著演化和 2,461 处响应标签集显著变化。
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.
@inproceedings{paperbot5804,
title = {Beware the evolving ‘intelligent’ web service! an integration architecture tactic to guard AI-first components},
author = {Alex Cummaudo and Scott Barnett and Rajesh Vasa and John Grundy and Mohamed Abdelrazek},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3409688}
}