Event logs are helpful to figure out what is happening in a system or to diagnose the causes that led to an unexpected crash or security issue. Unfortunately, their growing sizes and lacks of abstraction make them difficult to interpret, especially when a system integrates several communicating components. This paper proposes to learn models of communicating systems, e.g., Web service compositions, distributed applications, or IoT systems, from their event logs in order to help engineers understand how they are functioning and diagnose them. Our approach, called CkTail, generates one Input Output Labelled Transition System (IOLTS) for every component participating in the communications and dependency graphs illustrating another viewpoint of the system architecture. Compared to other model learning approaches, CkTail improves the precision of the generated models by better recognising sessions in event logs. Experimental results obtained from 9 case studies show the effectiveness of CkTail to recover accurate and general models along with component dependency graphs.
This paper addresses the problem of recovering behavioural models from IoT devices in order to help engineers understand how they are functioning and audit them. We present a model learning approach called ASSESS, which takes as inputs execution traces collected from IoT devices and generates models called systems of Labelled Transition Systems (LTSs). ASSESS generates as many LTSs as components integrated and identified into a device. The approach is specialised to IoT devices as it takes into account two architectures often used to integrate components with this kind of system (cyclic functioning, loosely-coupled or decoupled architectures). We experimented the approach on two IoT devices and an IoT gateway to evaluate the model conciseness and the approach efficiency.
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