Automated Unit Testing Practice Based on The Embedded Software Development Platform

Embedded software plays an essential role in modern technological systems, where quality and reliability are critical. However, traditional testing methods often face significant challenges in efficiency and coverage, creating a demand for more effective and comprehensive testing strategies. This s...

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Bibliographic Details
Main Authors: Yingbei, Niu, Soo See, Chai, Kok Luong \, Goh, Kim On, Chin
Format: Article
Language:en
Published: Karya Ilham Publishing 2025
Subjects:
Online Access:http://ir.unimas.my/id/eprint/50979/1/ARCAV39N1_P164_180.pdf
http://ir.unimas.my/id/eprint/50979/
https://karyailham.com.my/index.php/arca/article/view/465
https://doi.org/10.37934/arca.39.1.164180
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Summary:Embedded software plays an essential role in modern technological systems, where quality and reliability are critical. However, traditional testing methods often face significant challenges in efficiency and coverage, creating a demand for more effective and comprehensive testing strategies. This study aims to explore automated testing within embedded software development, focusing on its advantages, methodologies, erimental approach was adopted using automated unit testing tools to validate testing performance. The process covered functional, interface, user interface, and performance aspects. The results demonstrate that automated testing enables faster issue detection, improves precision, and enhances testing efficiency. Broad test coverage is achievable through well-structured automated unit testing, supported by best practices such as careful tool selection, clear and concise test scripting, early and continuous testing, and active stakeholder collaboration. Automated testing therefore offers a practical and efficient solution to improve software quality in embedded systems. Future research should examine the integration of automated and manual testing, security testing for embedded applications, and the application of machine learning and artificial intelligence to enhance testing capabilities.