Testing context-aware software systems from the voices of the automotive industry

As automotive software systems evolve towards high and full driving automation, evaluating their quality becomes increasingly challenging, especially concerning emerging behaviors. Context-awareness is the capability to sense the environment and adapt behavior. Automotive software systems are Context-Aware Software Systems (CASS). Previous secondary studies in technical literature indicate a need for testing techniques for CASS. However, these studies should have investigated the information provided by the industry. Therefore, this research undertakes a Gray Literature Study to uncover evidence of CASS testing using 20 reports from 16 automotive companies as primary sources. Our findings show that industry practices exhibit quality assurance best practices, but CASS abstraction adoption still needs to be completed. Industry reports emphasize testing challenges but lack technical resolutions, relying on amassing diverse datasets for testing. This research has the potential to impact the quality assurance of automotive software systems significantly and lead industry professionals to enhance their testing process.