Towards Machine Learning Support for Embedded System Tests
Stefan Scharoba, Kai-Uwe Basener, Jens Bielefeldt, Hans-Werner Wiesbrock, Michael Hübner · 2021
The correctness of embedded systems needs to be ensured by a high number of tests. Large amounts of data reflecting the system behavior are collected during these test runs. Automated test evaluations are often limited to checking very specific requirements which can hardly cover all possible kinds of erroneous behaviors. Manual examinations can compensate this by implicit knowledge of experienced test engineers but are very time-consuming and therefore costly.This paper shows how machine learning can support the evaluation of embedded system tests. Assessment of new test runs is based on available data from previous tests and aims at identifying those that deviate from usual behavior. Moreover the paper presents a generic approach that helps to find the most suitable detection algorithm in the given context. A case study proves the effectiveness of this approach. Quantitative comparisons show that our exploration is able to find solutions that outperform state of the art methods.