Automated Adaptation of Test Environments in Variant-Rich Development Scenarios

Context

Modern development processes, especially in the automotive domain, involve a large number of variants and frequent changes to software, hardware, and configurations. Test environments such as HiL (Hardware-in-the-Loop), SiL (Software-in-the-Loop), and MiL (Model-in-the-Loop) must be adapted accordingly to ensure valid and efficient testing. However, these adaptations are often manual, time-consuming, and error-prone. Goal of this thesis ist o addresses the need for early detection of variant or version changes and aims to automate the necessary adaptations to the corresponding test environments.

Targets

• Analyze typical adaptation needs in HiL, SiL, and MiL environments when variants change
• Develop a methodology to detect relevant changes early in the development or integration process
• Define rules or mappings for automatically adjusting test configurations and setups
• Evaluate the methodology with a real or representative test setup

Requirements

• Programming skills (e.g., Python, C#, C) are needed