DeepTest: How Machine Learning Can Improve the Test of Embedded Systems

Bielefeldt, Jens; Basener, Kai-Uwe; Khan, Siddique Reza; Massah, Mozhdeh; Wiesbrock, Hans-Werner; Scharoba, Stefan; Hübner, Michael

doi:10.1109/meco52532.2021.9460182

Cited by 3 publications

(1 citation statement)

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“…It is crucial to note that additional input data, when introduced automatically, must align with component behavior patterns to ensure the test corresponds to the system's actual behavior. Bielefeldt proposes employing deep learning to obtain new, realistic test data and identify new embedded system behavior patterns [14]. Augmented intelligence applied in embedded system testing serves as a robust tool acting as a competent and comprehensive expert system.…”

Section: Introductionmentioning

confidence: 99%

Automating the Analysis of Negative Test Verdicts: A Future-Forward Approach Supported by Augmented Intelligence Algorithms

Gnacy-Gajdzik,

Przystałka

2024

Applied Sciences

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In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing.

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Section: Introductionmentioning

confidence: 99%