Today customers are used to interconnected digital structures, such as smartphones, tablets and smart home systems.
Naturally, they expect that a modern can integrates seamlessly into the modern digital world.
Connecting the car with the digital environment, such as the smartphone or smart-home systems, is the most crucial factor for customer comfort.
Customers also expect the car's software to be adjustable, configurable and always up-to-date, just like other digital devices.
Strongly connected cars harbour security risks, which must be mitigated by fast and reliable update cycles.
Providing those update cycles for the car's software strongly challenges the established procedures and production hierarchies of OEMs and suppliers.

Fast update cycles and mechanisms to conveniently install updates and functionality on demand at any time or place are well-known in the domain of smartphones. With keywords such as "Update over the Air" and "Function on Demand", convenient mechanisms to install and update cars' software shall be introduced. However, a car is a safety-critical system. Therefore,  faulty software updates may critically affect the driver and other traffic participants.

Preventing faulty updates of cars requires efficient and effective test procedures. In addition, the ever-increasing diversity between car variants, based on growing software parts in the automotive systems, requires consequent variability management to enable efficient testing processes.

The project EasyUpdate, in cooperation with an OEM, aims to increase the update ability of cars in the field and newly developed EE systems. The essential part of the cooperation focuses on improving the variability management in the industry. Therefore, the following topics are considered:

• Analysis and assessment of updates in the variability and architecture space wrt. risk and testing effort
• Development of efficient testing strategies for updates in variability and architecture space.
• Adaptation of the developed analysis and testing strategies to evolving configurable systems.

Student theses

• Tim Witschel. "Generating Optimized Samples with Attributed Feature Models." Masterthesis 2021. Supervision: Marc Hentze, Tobias Pett.
• Marcel Tschada. "Generating Taxonomies of Feature-Model Anomalies." Bachelorthesis 2021. SupervisionMarc Hentze, Tobias Pett.
• Karl Störmer. "T-wise Interaction Samples for Automotive Systems using Solution-Space Sampling." Masterthesis 2022. Supervision: Marc Hentze, Tobias Pett
• Julien Best. "Influence of the Feature-Model Structure on Configuration Sampling." Bachelorthesis 2022. Supervision: Marc Hentze, Tobias Pett
• Melissa Heumann. "Tool Support for Generating Feature Models of Automotive Systems" Bachelorthesis 2022. Supervision: Marc Hentze, Tobias Pett

Publications

• Tobias Pett, Domenik Eichhorn, and Ina Schaefer. 2020. Risk-based compatibility analysis in automotive systems engineering. Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings. Association for Computing Machinery, New York, NY, USA, Article 34, 1–10. https://doi.org/10.1145/3417990.3421263
• Tobias Pett, Sebastian Krieter, Tobias Runge, Thomas Thüm, Malte Lochau, and Ina Schaefer. 2021. Stability of Product-Line Samplingin Continuous Integration. In 15th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS'21). Association for Computing Machinery, New York, NY, USA, Article 18, 1–9. https://doi.org/10.1145/3442391.3442410
• Marc Hentze, Tobias Pett, Thomas Thüm, and Ina Schaefer. 2021. Hyper Explanations for Feature-Model Defect Analysis. In 15th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS'21). Association for Computing Machinery, New alysis. In 15th International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS'21). Association for Computing Machinery, New York, NY, USA, Article 14, 1–9. https://doi.org/10.1145/3442391.3442406