Variant-aware Reconfiguration of Automotive Virtual Test Environments

Modern vehicles offer a multitude of features, adapting to individual preferences. Addressing the challenge of efficiently testing a wide range of automotive system variants within a virtual test environment is crucial in the face of the growing complexity of these vehicles. With numerous configuration options and frequent software updates, there's a pressing need for a standardized approach to variant-aware reconfiguration.
In response to this challenge, we propose a delta-based reconfiguration of a virtual test environment with modular components. This reconfiguration process allows a dynamic and efficient adaption based on selected features of the module under test. Leveraging feature models, we define relationships between features and their impact on the mut and test environment configuration.
Our paper introduces a concept for variant-aware reconfiguration. This concept utilizes a 150% model, representing a superset of a module containing all necessary assets to derive any variant. Additionally, we employ delta analysis to reduce reconfiguration efforts by identifying only the necessary adjustments based on changes in selected features.