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.

翻译：低代码平台（LCPs）凭借其加速软件开发的承诺，正在各行业日益普及。然而，其采用仍面临诸多障碍。其中，供应商锁定是一个主要问题，尤其考虑到这些平台之间缺乏互操作性。通常，在一个LCP中对应用程序建模后，若需迁移至另一平台，则必须在新平台中从零开始重新建模所有内容（数据模型、图形用户界面、工作流等）。为克服这一困境，本研究提出一种通过（半）自动迁移平台间模型来提升LCP互操作性的方法。具体迁移路径取决于源工具与目标工具的功能特性。我们首先分析主流LCP，归纳其导入导出方案，并在数据格式可用时定义其间的转换规则。随后引入基于LLM的增强方案：利用大语言模型的图像识别功能，通过当前模型的简易图像输出来实现模型迁移。完整流程在BESSER建模框架上实现，该框架充当工具间的枢轴表示层。