We provide on this page the different artefacts which have been used and created when performing the case study concerning the traffic light system.
Abstract
In model-driven engineering (MDE), models are mostly used in prescriptive ways for system engineering. While prescriptive models are indeed an important ingredient to realize a system, for later phases in the systems’ lifecycles additional model types are beneficial to use. Unfortunately, current MDE approaches mostly neglect the information upstream in terms of descriptive models from operations to design, which would be highly needed to improve systems continuously. To tackle this limitation, we propose execution-based model profiling as a continuous process to improve prescriptive models at design-time through runtime information by incorporating knowledge in form of profiled metadata from event logs. For this purpose we combine techniques of process mining (PM) with runtime models of MDE. In the course of a case study, we implement a preliminary prototype of our approach based on a traffic light system example which shows the feasibility and benefits of combining MDE and PM.
Case Study Description
The case study is about enhancing an existing MDE solution with the automatic generation of descriptive models concerning the operation of a system, in our concrete case a traffic light system.
In the following we provide the model of the system and the generated Python code. The Python code is instrumented in order to produce observation models which are managed by a logging micro service and stored in the NeoEMF persistency solution.
In order to analyse the observations, we make use of standard process mining techniques and tools. Therefore, we transform the observation models into workflow models which can be automatically imported in existing process mining tools. For the transformations, we employ the ATLAS Transformation Language (ATL).
All menioned artefacts of the case study are provided as open source.
Downloads
- Design Model: model.zip (59 downloads)
- Generated Runtime Models (Observation, Workflow, Petri Net): RuntimeModels.zip (38 downloads)
- Observation Metamodel: Observation_Metamodel.zip (34 downloads)
- Workflow Metamodel: WF_Metamodel.zip (38 downloads)
- XES Metamodel: XES_Metamodel.zip (40 downloads)
- Eclipse project including the Logging Micro Service: MicroService.zip (103 downloads)
- Eclipse project including the ATL transformations for generating workflows from observations
- State Occurrences 2 WF: StateSeq2Workflow.zip (34 downloads)
- Value Occurrences 2 WF: ValSeq2Workflow.zip (40 downloads)
Running the Case Study
For running the case study, we recommend to use the Eclipse Modeling Edition in combination with different plugins. In the following we provide information on how to download, run and setup the Eclipse bundle for elaborating the case study.
Download Eclipse
First you have to download the Eclipse Modeling Tools (version: Neon R) for your operating system and architecture. Once the download is finished, unzip the downloaded archive to any location you prefer. Please note, however, that the location should be writable without additional user permissions so that Eclipse may autonomously install updates and additional plug-ins.
Run Eclipse
The prerequisite for running Eclipse is a current version of the Java Runtime Environment. Eclipse itself does not have to be “installed” per se. You may directly start Eclipse by running eclipse (.exe in case you use Windows). Select a workspace location according to your personal preferences.
Install Required Plug-Ins
For elaborating the case study, you need to install the following additional plug-ins: