Abstract: A device may receive an application for transforming legacy applications into low-code/no-code applications to be managed by a low-code/no-code platform, and may execute the application for a legacy application of the legacy applications. The device may process the legacy application, with a machine learning model, to identify one or more components of the legacy application to be managed by the low-code/no-code platform, and may transform the one or more components into one or more transformed components to be managed by the low-code/no-code platform. The device may implement the one or more transformed components in the legacy application to generate a transformed legacy application, and may perform one or more actions based on the transformed legacy application.

Abstract: A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.

Abstract: A system for generating an architecture diagram includes an input processor, a machine learning processor, and an advice generator. The input processor is configured to receive, from a terminal, entity data associated with a plurality of entities of an architecture and path data associated with a plurality of paths that correspond to interconnections between the plurality of entities. The machine learning processor utilizes a training dataset to assess whether the entities defined by the entity data are correctly interconnected as defined by the path data. The advice generator receives the assessment from the machine learning processor, prepares a recommendation based on the assessment, and communicates the recommendation to the terminal. User feedback is represented in the training data to improve the relevancy of the recommendation.