Towards the interoperability of low-code platforms

TL;DR

This paper proposes a semi-automated approach to enhance interoperability among low-code platforms by analyzing data formats, defining transformations, and employing large language models for model migration, all integrated into the BESSER framework.

Contribution

It introduces a novel method combining data format transformations and LLM-based image recognition to facilitate model migration across low-code platforms.

Findings

Analyzed popular LCPs and characterized their import/export options.

Developed transformation techniques for data format compatibility.

Implemented a pipeline using LLMs and the BESSER framework for model migration.

Abstract

With the promise of accelerating software development, low-code platforms (LCPs) are becoming popular across various industries. Nevertheless, there are still barriers hindering their adoption. Among them, vendor lock-in is a major concern, especially considering the lack of interoperability between these platforms. Typically, after modeling an application in one LCP, migrating to another requires starting from scratch remodeling everything (the data model, the graphical user interface, workflows, etc.), in the new platform. To overcome this situation, this work proposes an approach to improve the interoperability of LCPs by (semi)automatically migrating models specified in one platform to another one. The concrete migration path depends on the capabilities of the source and target tools. We first analyze popular LCPs, characterize their import and export alternatives and define transformations between those data formats when available. This is then complemented with an LLM-based solution, where image recognition features of large language models are employed to migrate models based on a simple image export of the model at hand. The full pipelines are implemented on top of the BESSER modeling framework that acts as a pivot representation between the tools.