TAOS-CI: Lightweight & Modular Continuous Integration System for Edge Computing

TL;DR

TAOS-CI is a lightweight, modular continuous integration system designed for edge computing and consumer electronics, enabling frequent updates and regression prevention across diverse device models.

Contribution

It introduces a portable, modular CI platform tailored for edge devices that improves efficiency and resource usage compared to previous methods.

Findings

Significantly reduces plugin execution time.

Consumes less memory than previous approaches.

Easily adaptable to various source code repositories.

Abstract

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.