Low-code development platforms (LCDPs) are easy to use visual environments that are being increasingly introduced and promoted by major IT players to permit citizen developers to build their software systems even if they lack a programming background. Understanding and evaluating the LCDP to be employed for the particular problem at hand are difficult tasks mainly because decision-makers have to choose among hundreds of heterogeneous platforms, which are difficult to evaluate without dedicated support. Thus, a detailed classification is needed to elaborate on the existing low-code platforms and to help users find out the most appropriate platforms based on their requirements.In this paper, a technical survey of different LCDPs is presented by relying on a proposed conceptual comparative framework. In particular, by analyzing eight representative LCDPs, a corresponding set of features have been identified to distil the functionalities and the services that each considered platform can support. The final aim is facilitating the understanding and the comparison of the lowcode platforms that can best accommodate given user requirements.

Low-code development platforms (LCDPs) permit developers that do not have strong programming experience to produce complex software systems. Visual environments permit to specify workflows consisting of sequential or parallel executions of services that are directly available in the considered LCDP or are provided by external entities. Specifying workflows involving different LCDPs and services can be a difficult task. In this paper, we propose the adoption of concepts and tools related to the composition of model transformations to support the specification of complex workflows in LCDPs. We elaborate on how LCDPs services can be considered as model transformations and thus, workflows of services can be considered as model transformation compositions. The architecture of the environment supporting the proposed solution is presented. CCS CONCEPTS• Software and its engineering → Abstraction, modeling and modularity; Model-driven software engineering.

Forecasting a time series is an ever growing area in which various machine learning techniques have been used to predict and analyze the future based on the data gathered from past. “Prophet” forecasting model is the most recent development in forecasting the time series, developed by Facebook. Prophet is much faster and simpler to implement than the previous forecasting model such as ARIMA model. Classification of forecasting output can be done by applying convolution neural network (CNN) on the outcomes of the Prophet model. To get higher accuracy with lesser loss, the method runs CNN with the best possible deep layers. The yearly, weekly, daily seasonality and trends could be realized by Prophet Model. The paper shows classification of these output based on the varying types of seasonality and trends. The labeled output can then, train and test all the trends’ result and find out the accuracy and loss incurred in a CNN model. Applying different depth and parameters of CNN that is a combined unit at each layer, it can achieve more than 96% accuracy with less than 4% loss. The integration of prophet and CNN shows that the training and testing model of a neural network can validate the prediction done by using prophet forecasting model along with the seasonality and trends parameters are in coherence to one another.

Low-code development platforms (LCDPs) aim to simplify software systems' development by providing easy-to-use graphical interfaces and drag-and-drop facilities. The system behaviors are defined through available data handling and workflow mechanisms enabling the specification of business processes from users that do not have strong programming skills. However, the number of LCDPs has grown significantly over the last few years. Consequently, it is not easy for inexpert users to understand their differences, especially in terms of provided modeling constructs. In this article, we analyze and compare eight low-code development platforms by focusing on their capabilities for specifying business processes. The analysis exploits business process modeling and notation (BPMN) as a reference modeling language. Thus, the core elements of BPMN are leveraged to analyze the workflow mechanisms provided by each of the analyzed LCDP. The article explains different types of process flows and data handling means of the different LCDPs aiming to give potential users objective elements that can be used to make educated decisions when selecting LCDPs. K E Y W O R D S business process management, low-code development platforms, model-driven engineering 1 * Hereafter, the terms low-code platforms and low-code development platforms are used interchangeably and are abbreviated as LCDPs.

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