Conference of Digital Ecosystems of the Future: Methods, Techniques and Applications (EMISA), May 15-17, 2019, Tutzing, Germany.
With new advances such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), more and more discrete software systems interact with continuous physical systems. State machines are a classical approach to specify the intended behavior of discrete systems during development. However, the actual realized behavior may deviate from those specified models due to environmental impacts, or measurement inaccuracies. Accordingly, data gathered at runtime should be validated against the specified model. A first step in this direction is to identify the individual system states of each execution of a system at runtime. This is a particular challenge for continuous systems where system states may be only identified by listening to sensor value streams. A further challenge is to raise these raw value streams on a model level for checking purposes. To tackle these challenges, we introduce a model-driven runtime state identification approach. In particular, we automatically derive corresponding time-series database queries from state machines in order to identify system runtime states based on the sensor value streams of running systems. We demonstrate our approach for a subset of SysML and evaluate it based on a case study of a simulated environment of a five-axes grip-arm robot within a working station.