TAOS-CI：面向边缘计算的轻量级模块化持续集成系统
TAOS-CI: Lightweight & Modular Continuous Integration System for Edge Computing
摘要：
随着物联网和边缘设备的激增，我们观察到许多消费电子产品正在成为另一种物联网和边缘设备。与传统的智能设备（诸如智能电话）不同，消费电子产品通常具有每个产品型号的设备数量较少的显著差异。在这种高分散性的情况下，边缘设备的扩散需要消费电子产品的频繁且无缝的更新，这使得制造商倾向于回归，因为制造商在每个软件发布实例中具有较少的资源;即，他们需要通过产品型号的数量乘以更新的数量来重复发布。持续集成（CI）系统可以帮助防止回归错误主动开发软件包，包括频繁更新的设备软件平台。拟议的CI系统提供了一个便携式和模块化软件平台，自动检查启用模块的传入更改的潜在问题：代码格式和样式，性能回归，对源代码的静态检查，构建和打包测试，以及在物联网和边缘设备上部署平台映像之前对构建的二进制文件进行动态检查。此外，我们提出的方法是轻量级的，足以托管在普通的台式计算机，甚至几十个开发人员。因此，它可以很容易地应用于许多不同的源代码存储库。评估结果表明，所提出的方法大大提高了插件的执行时间和内存消耗，与以前的研究方法相比。
英文摘要：
With the proliferation of IoT and edge devices, we are observing a lot of consumer electronics becoming yet another IoT and edge devices. Unlike traditional smart devices, such as smart phones, consumer electronics, in general, have significant diversities with fewer number of devices per product model. With such high diversities, the proliferation of edge devices requires frequent and seamless updates of consumer electronics, which makes the manufacturers prone to regressions because the manufacturers have less resource per an instance of software release; i.e., they need to repeat releases by the number of product models times the number of updates. Continuous Integration (CI) systems can help prevent regression bugs from actively developing software packages including the frequently updated device software platforms. The proposed CI system provides a portable and modular software platform automatically inspecting potential issues of incoming changes with the enabled modules: code format and style, performance regressions, static checks on the source code, build and packaging tests, and dynamic checks with the built binary before deploying a platform image on the IoT and edge devices. Besides, our proposed approach is lightweight enough to be hosted in normal desktop computers even for dozens of developers. As a result, it can be easily applied to a lot of various source code repositories. Evaluation results demonstrate that the proposed method drastically improves plug-ins execution time and memory consumption, compared with methods in previous studies.
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。
