Logging still is a core functionality used to understand the behavior of programs and executable models. Yet, modeling languages rarely consider logging as a first-level activity that is manifested in the language through modeling elements or their behavior. When logging is part of the code generated for the respective models or the corresponding runtime environment only, it must be generic, as the modeler cannot influence, through the models, what and when logging takes place. To enable modelers to log model behavior, we devised a method based on language extension and smart code generation that can integrate logging into arbitrary textual modeling languages. Based on this method, log entries can be produced, traced, and presented through a web application. This method and its infrastructure can facilitate lifting logging to the model level and, hence, improve the understanding of executable models.

Developers questioning why their system behaves differently than expected often have to rely on time-consuming and error-prone manual analysis of log files. Understanding the behavior of Internet of Things (IoT) applications is a challenging task because they are not only inherently hard-to-trace distributed systems, but their integration with the environment via sensors adds another layer of complexity. Related work proposes to record data during the execution of the system, which can later be replayed to analyze the system. We apply the model-driven development approach to this idea and leverage digital twins to collect the required data. We enable developers to replay and analyze the system’s executions by applying model-to-model transformations. These transformations instrument component and connector (C&C) architecture models with components that reproduce the system’s environment based on the data recorded by the system’s digital twin. We validate and evaluate the feasibility of our approach using a heating, ventilation, and air conditioning (HVAC) case study. By facilitating the reproduction of the system’s behavior, our method lowers the barrier to understanding the behavior of model-driven IoT systems.