ESR 9: DevOps Support for Low-Code Engineering Platforms
Tackling the challenge of managing the full life-cycle of software systems requires a well-defined mix of approaches.
While in the early phases model-driven approaches are frequently used to design systems, in the later phases data-driven approaches are used to reason on different key performance indicators of systems under operation. This immediately poses the question how operational data can be mapped back to design models to evaluate existing designs and to reason about future re-designs.
This is also reflected in the current DevOps movement to better synchronize the software development with IT administration and operation. Of course, this is of particular importance in long-living systems such as industrial automation systems or domains where frequent requirement changes are expected due to missing information in the development phase or rapidly changing user behaviour.
The main objective of this project is to provide a generic methodology to harmonize model-based and measurement-based approaches. In particular, a low-code engineering framework is required which also supports runtime data management and analytics to reason about runtime properties of systems which are derived from and aligned with design models. Having this systematic generation of data management and analytics opens the door to analyse data through design models which acts as a common communication model between development and operation. Having such a framework is of particular importance to reason also about possible design improvements for which exploration techniques can make use of the data analytics capabilities by running simulations before deploying the improvements in the operational settings.
The goal of the project is to provide a generic methodology for LCEPs to derive a runtime data management and analytics capabilities which fills the gap between software development and IT administration and operation. The project will develop an open-source framework that is able to express runtime concerns in models as well as to analyse these concerns during operation. Finally, this framework will enriched by an execution platform for highly-scalable, distributed design space exploration algorithms which make also use of the data analytics by simulation techniques.
This position has been filled.
Please note that the vacancy on the institutionnal website must be considered as the official version of this PhD position.