More and more applications (apps) are entering vehicles. Customers would like to have in-car apps in their infotainment system, which they already use regularly on their smartphones. Other apps with new functionalities also inspire vehicle customers, but only as long as the customer can utilize them. To ensure customer satisfaction, it is important that these apps work and that failures are found and corrected as quickly as possible. Therefore, in-car apps also implicate requirements for future vehicle diagnostics. This is because current vehicle diagnostic methods are not designed for handling dynamic software failures of apps. Consequently, new diagnostic methods are needed to support the diagnosis of in-car apps. Log data are a central building block in software systems for system health management or troubleshooting. However, there are different types of log data and log environment setups depending on the underlying system or software platform. Depending on that, the creation of log data takes place with different logging approaches, leading to heterogeneous results that complicates the analysis of log data. In order to classify different types of log data, a taxonomy for log data is derived systematically in this paper. This taxonomy is deduced from identified challenges and heterogeneity regarding logging and log data. Furthermore, the taxonomy is applied to evaluate four logging frameworks for vehicle diagnostics based on three software platforms that are commonly used to operate in-car apps within vehicles: Android, AUTOSAR Adaptive, and Java Standard Edition (SE). As these platforms generate different types of log data, this leads to determining and compare the differences between these frameworks and their commonalities for deployment in vehicles. In addition, the evaluation offers potential starting points for future work regarding the utilization of log data for future vehicle diagnostics and related methods.
