Embedded systems hold immense potential, but their integration into advanced devices comes with significant costs. Malfunctions in these systems can result inequipment failures, posing serious risks and potential accidents. To ensure theirproper functionality, embedded system components undergo rigorous testing phases,which can be time-consuming, especially for components with numerous connections. Therefore, it is crucial to reduce test time while maintaining high-qualitytesting to detect and address failures early in the development cycle, resulting in improved and safer products. This report delves into various techniques and algorithms aimed at expediting testingprocesses, such as machine learning, risk analysis, test parallelization, and combinatorial testing. It examines the practicality of mathematical models and automatedapproaches in real-world companies through experimentation and implementation.In essence, the report tackles the challenges involved in testing embedded systems,explores different approaches to reduce test time, and presents a suitable model formaintaining test quality. The ultimate goal is to present and implement a methodthat effectively reduces test time while upholding an acceptable level of test quality.The obtained results provide valuable insights for future test groups and researchersseeking to optimize their testing processes and deliver safer products

Characterisation of modern complex powertrains is a time consuming and expensive process. Little effort has been made to improve the efficiency of testing methodologies used to obtain data for this purpose. Steady-state engine testing is still regarded as the golden standard, where approximately 90% of testing time is wasted waiting for the engine to stabilize. Rapid dynamic engine testing, as a replacement for the conventional steady-state method, has the potential to significantly reduce the time required for characterisation. However, even by using state of the art measurement equipment, dynamic engine testing introduces the problem that certain variables are not directly measurable due to the excitation of the system dynamics. Consequently, it is necessary to develop methods that allow the observation of not directly measurable quantities during transient engine testing. Engine testing for the characterisation of the engine air-path is specifically affected by this problem since the air mass flow entering the cylinder is not directly measurable by any sensor during transient operation. This dissertation presents a comprehensive methodology for engine air charge characterisation using dynamic test data. An observer is developed, which allows observation of the actual air mass flow into the engine during transient operation. The observer is integrated into a dual-ramp testing procedure, which allows the elimination of unaccounted dynamic effects by averaging over the resulting hysteresis. A simulation study on a 1-D gas dynamic engine model investigates the accuracy of the developed methodology. The simulation results show a trade-off between time saving and accuracy. Experimental test result confirm a time saving of 95% compared to conventional steady-state testing and at least 65% compared to quasi steady-state testing while maintaining the accuracy and repeatability of conventional steady-state testing.