Scientific publications

A well-known conformance relation for model-based testing is ioco. A conformance relation expresses when an implementation is correct with respect to a specification. Unlike many other conformance and refinement relations, ioco has different domains for implementations and for specifications. Consequently, ioco is neither reflexive nor transitive, implying that a specification does not implement itself, and that specifications cannot be compared for refinement.

Efficient code generation for image processing applications continues to pose a challenge in a domain where high performance is often necessary to meet real-time constraints. The inherently complex structure found in most image-processing pipelines, the plethora of transformations that can be applied to optimize the performance of an implementation, as well as the interaction of these optimizations with locality, redundant computation and parallelism, can be indentified as the key reasons behind this issue.

To handle the rigid power and energy constraints in the Digital BaseBand (DBB) of Wireless Sensor Networks (WSN)s, we introduce approximate computing as a new power reduction method. The Received Signal Strength Indicator (RSSI) computation is a key element in DBB processing. We evaluate the trade-off in RSSI computation between Quality-of-Service (QoS) and power consumption through circuit-level approximation.

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.

This paper covers the Rigorous Examination of Reactive Systems (RERS) Challenge 2019. For the first time in the history of RERS, the challenge features industrial tracks where benchmark programs that participants need to analyze are synthesized from real-world models. These new tracks comprise LTL, CTL, and Reachability properties.

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.

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.

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.