We present BOCoSy, a tool for Bounded symbolic Output-feedback Controller Synthesis. Given a specification, BOCoSy synthesizes symbolic output-feedback controllers which interact with a given plant via a pre-defined finite symbolic interface. BOCoSy solves this problem by a new lazy abstraction-refinement technique which starts with a very coarse abstraction of the external trace semantics of the given plant and iteratively removes non-admissible behavior from this abstract model until a controller is found. BOCoSy steers the search for controllers towards small and concise state space representations by utilizing ideas from bounded synthesis. As a result, BOCoSy returns small and explainable controllers that are still powerful enough to solve the given synthesis problem. We show that BOCoSy is able to synthesize small, human readable symbolic controllers quickly on a set of benchmarks.

Abstract Stream-based runtime monitors are used in safety-critical applications such as Unmanned Aerial Systems (UAS) to compute comprehensive statistics and logical assessments of system health that provide the human operator with critical information in hand-over situations. In such applications, a visual display of the monitoring data can be much more helpful than the textual alerts provided by a more traditional user interface. This visualization requires extensive real-time data processing, which includes the synchronization of data from different streams, filtering and aggregation, and priorization and management of user attention. We present a visualization approach for theRTLolamonitoring framework. Our approach is based on the principle that the necessary data processing is the responsibility of the monitor itself, rather than the responsibility of some external visualization tool. We show how the various aspects of the data transformation can be described asRTLolastream equations and linked to the visualization component through a bidirectional synchronous interface. In our experience, this approach leads to highly informative visualizations as well as to understandable and easily maintainable monitoring code.

With ever increasing autonomy of cyber-physical systems, monitoring becomes an integral part for ensuring the safety of the system at runtime. $$\text {StreamLAB} $$ is a monitoring framework with high degree of expressibility and strong correctness guarantees. Specifications are written in $$\text {RTLola} $$ , a stream-based specification language with formal semantics. $$\text {StreamLAB} $$ provides an extensive analysis of the specification, including the computation of memory consumption and run-time guarantees. We demonstrate the applicability of $$\text {StreamLAB} $$ on typical monitoring tasks for cyber-physical systems, such as sensor validation and system health checks.