Scientific publications

Autonomous vehicles use Electronic Control Units running complex software to improve passenger comfort and safety. To test safety of in-vehicle electronics, the ISO 26262 standard on functional safety recommends using fault injection during component and system-level design. A Fault Injection Framework (FIF) induces hard-to-trigger hardware and software faults at runtime, enabling analysis of fault propagation effects.

More and more high tech companies are struggling with the maintenance of legacy software. Legacy software is vital to many organizations, so even if its behavior is not completely understood it cannot be thrown away. To re-factor or re-engineer the legacy software components, the external behavior needs to be preserved after replacement so that the replaced components possess the same behavior in the system environment as the original components.

Gaining insight in the properties of an Internet of Things (IoT) system during the design phase is difficult. The cosimulation of such a system would be very useful, but creating it is usually time consuming. By means of domain specific languages (DSLs) we support the fast construction of large co-simulations of IoT systems.

Kahn and MacQueen have introduced a generic class of determinate asynchronous data-flow applications, called Kahn Process Networks (KPNs) with an elegant mathematical model and semantics in terms of Scott-continuous functions on data streams together with an implementation model of independent asynchronous sequential programs communicating through FIFO buffers with blocking read and non-blocking write operations.

Inferring behavioral models (e.g., state machines) of software systems is an important element of re-engineering activities. Model inference techniques can be categorized as active or passive learning, constructing models by (dynamically) interacting with systems or (statically) analyzing traces, respectively.

The booming popularity of data science is also affecting high-tech industries. However, since these usually have different core competencies - building cyber-physical systems rather than e.g. machine learning or data mining algorithms - delving into data science by domain experts such as system engineers or architects might be more cumbersome than expected.

The IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol defines two types of timeslots for communications, namely dedicated and shared timeslots. An upper layer in the protocol stack uses these timeslots to design a communication schedule for the network links, based on the required bandwidth for each link.

Connected high-tech systems allow the gathering of operational data at unprecedented volumes. A direct benefit of this is the possibility to extract usage models, that is, a generic representations of how such systems are used in their field of application. Usage models are extremely important, as they can help in understanding the discrepancies between how a system was designed to be used and how it is used in practice.

IoT applications and other distributed control applications are characterized by the interaction of many hardware and software components. The inherent complexity of the distributed functionality introduces challenges on the detection and correction of issues related to functionality or performance, which are only possible to do after system prototypes or pilot installations have been built.

Cyber-physical systems consist of many hardware and software components. Over the life-cycle of these systems, components are replaced or updated. To avoid integration problems, good interface descriptions are crucial for component-based development of these systems. For new components, a Domain Specific Language called Component Modeling Analysis can be used to formally define the interface of such a component in terms of its signature, state and timing behavior.