Towards LLM-Powered Consistency in Model-Based Low-Code Platforms

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Titel: Towards LLM-Powered Consistency in Model-Based Low-Code Platforms
Autoren: Hagel, Nathan, Hili, Nicolas, Bartel, Alexander, Koziolek, Anne
Quelle: 2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C). :364-369
Verlagsinformationen: IEEE, 2025.
Publikationsjahr: 2025
Schlagwörter: ddc:004, LLM, DSL, low-code development platform, consistency, AI, model-driven engineering, DATA processing & computer science, meta-model
Beschreibung: Low-code platforms often use various models that define the application built by citizen developers. With the increasing size and complexity of the applications built using low-code platforms, the number of required models and the dependencies between them expand. However, with increased complexity, keeping these models consistent during the development or evolution of the application is crucial and often a non-trivial task for citizen developers. In this paper, we present four approaches to how LLMs can be used and integrated on the architecture level into low-code platforms to (i) create consistent models automatically, (ii) keep models consistent based on different types of dependencies, and (iii) support users in maintaining consistent models during the development. We implemented the approaches in a prototype and evaluated them in an exploratory study. The results show that state-of-the-art LLMs are capable of preserving consistency for low-code models as well as generating correct and consistent models in various scenarios.
Publikationsart: Article
Conference object
Dateibeschreibung: application/pdf
DOI: 10.1109/icsa-c65153.2025.00058
DOI: 10.5445/ir/1000180779
Zugangs-URL: https://publikationen.bibliothek.kit.edu/1000180779
https://doi.org/10.5445/IR/1000180779
https://publikationen.bibliothek.kit.edu/1000180779/158661758
Rights: STM Policy #29
Dokumentencode: edsair.doi.dedup.....f7526f46a5ff9741d3a93329ec0772c1
Datenbank: OpenAIRE
Beschreibung
Abstract:Low-code platforms often use various models that define the application built by citizen developers. With the increasing size and complexity of the applications built using low-code platforms, the number of required models and the dependencies between them expand. However, with increased complexity, keeping these models consistent during the development or evolution of the application is crucial and often a non-trivial task for citizen developers. In this paper, we present four approaches to how LLMs can be used and integrated on the architecture level into low-code platforms to (i) create consistent models automatically, (ii) keep models consistent based on different types of dependencies, and (iii) support users in maintaining consistent models during the development. We implemented the approaches in a prototype and evaluated them in an exploratory study. The results show that state-of-the-art LLMs are capable of preserving consistency for low-code models as well as generating correct and consistent models in various scenarios.
DOI:10.1109/icsa-c65153.2025.00058