Testing of AI-based systems such as autonomous vehicles is challenging due to many situations and scenarios. Brute force is expensive and has gaps, as we see in practice. We thus use synthetic data for an AI-driven testing. This data covers real-world scenarios to train autonomous systems in a simulation-based environment. The training success is evaluated in a data loop and enhanced to close blind spots and unknown knowns. This thesis targets to integrate a requirements and test engine to an automated test system.
The goal of the thesis is to integrate existing parts of the system. A fully running system shall be implemented. The integration comprises verification and validation checks for the existing parts. Professional tools such as DOORS shall be used for industry-scale AI-based testing of autonomous systems.
Knowledge in Python Industry-scale software engineering and tools Work in a self-independent way Passionate about clean and good quality code Capable of integrating your work with other parts of the system
Christof Ebert
The aim of the project is to identify the different drivers of complexity within this project and quantify it with appropriate measures. We focus on the change of the software part. Specifically, we want a methodology that predicts the complexity of changing a given software module. In order to assist the management of the Digital Twin, an assistant system is to be created, which assesses the software complexity based on the described aspects. The feasibility of the assistant system will be evaluated on the software stack of the Digital Twin. Since we will have five different implementations of the same problem from the lab courses, there will be test data available to check the assessment results for plausibility.
The Master Project should first analyze the literature for drivers of complexity and established complexity measurement methods in order to derive a methodology that identifies the complexity drivers and quantifies them. Moreover, within the project, an assistant system will be developed that assesses the complexity and visualizes the results. The assistant system will be evaluated using different variants of the Digital Twin.
Independent, scientific work Very good math and programming skills Good English skills
Golsa Ghasemi