Paper
8 December 2023 Embedded software test case design based on black box technology
Zhihong Zhang
Author Affiliations +
Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430Q (2023) https://doi.org/10.1117/12.3014580
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China
Abstract
Traditional software use case testing usually writes test cases based on requirements or design documents, but the writing of test cases often cannot cover all possible situations and paths. Therefore, this article aims to study and explore the embedded software test case design method based on black box technology. This article adopts a new test case design method, which combines multiple test case design techniques such as equivalence class division, boundary value analysis, decision table testing and state transition testing. At the same time, this article also designs a set of comprehensive and effective test cases in combination with the actual embedded software testing requirements and scenarios. In the process of test case design, this article also considers the key issues of test case repeatability, verifiability and scalability. Through the actual test of the designed test cases, the correct rate of the research method in this paper is 89%, 93%, 91% and 90% in four experiments respectively. The test cases designed in this article can effectively cover the various functions and performance requirements of embedded software, and can find multiple defects and errors in the software at the same time. Compared with the traditional test case design method, the test cases designed in this article are more comprehensive and accurate, and the test cost is also effectively controlled.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Zhihong Zhang "Embedded software test case design based on black box technology", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430Q (8 December 2023); https://doi.org/10.1117/12.3014580
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