Abstract. Creating a valid software configuration of a product line can require laborious customizations involving multiple configuration file types, such as feature models, domain-specific languages, or preprocessor defines in C header files. Using configurable off-the-shelf components causes additional complexity. Without checking of constraints across file types boundaries already at configuration time, intricate inconsistencies are likely to be introduced-resulting in product defects, which are costly to discover and resolve later on. Up to now, at best ad-hoc solutions have been applied. To tackle this problem in a general way, we have developed an approach and a corresponding plug-in infrastructure. It allows for convenient definition and checking of constraints across configuration file types and product line boundaries. Internally, all configuration files are converted to models, facilitating the use of model-based constraint languages (e.g., OCL). Converter plug-ins for arbitrary configuration file types may be integrated and hide a large amount of complexity usually associated with modeling. We have validated our approach using a quadrotor helicopter product line comprising three sub-product-lines and four different configuration file formats. The results give evidence that our approach is practically applicable, reduces time and effort for product derivation (by avoiding repeated compiling, testing, and reconfiguration cycles), and prevents faulty software deployment.

No abstract

No abstract

Benchmark Proposal: The implementation of digital control systems in complex multi- core or distributed real-time systems results in non-deterministic input/output timing. Such timing deviations typically lead to degraded performance or even instability, which in turn may jeopardize safety goals. We present the problem of proving worst-case guarantees for given input/output timing bounds as a benchmark for the verification of hybrid dynamical systems.

Abstract-Robotics systems usually comprise sophisticated sensor and actuator systems with no less complex control applications. These systems are subject to frequent modifications and extensions and have to adapt to their environment. While automation systems are tailored to particular production processes, autonomous vehicles must adaptively switch their sensors and controllers depending on environmental conditions. However, when designing and implementing the process control system, traditional control theory focuses on the control problem at hand without having this variability in mind. Thus, the resulting models and implementation artefacts are monolithic, additionally complicating the real-time system design.In this paper, we present a modularisation approach for the design of robotics process control systems, which not only aims for variability at design-time but also for adaptivity at run-time. Our approach is based on a layered control architecture, which includes an explicit interface between the two domains involved: control engineering and computer science. Our architecture provides separation of concerns in terms of independent building blocks and data flows. For example, the replacement of a sensor no longer involves the tedious modification of downstream filters and controllers. Likewise, the error-prone mapping of high-level application behaviour to the process control system can be omitted. We validated our approach by the example of an autonomous vehicle use case. Our experimental results demonstrate ease of use and the capability to maintain quality of control on par with the original monolithic design.