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Calculating differences between models is an important and challenging task in Model Driven Engineering. Model differencing involves a number of steps starting with identifying matching model elements, calculating and representing their differences, and finally visualizing them in an appropriate way. In this paper, we provide an overview of the fundamental steps involved in the model differencing process and summarize the advantages and shortcomings of existing approaches for identifying matching model elements. To assist potential users in selecting one of the existing methods for the problem at stake, we investigate the trade-offs these methods impose in terms of accuracy and effort required to implement each one of them.

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.

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It is of critical relevance that designers are able to comprehend the various kinds of design-level modifications that a system undergoes throughout its entire lifecycle. In this respect, an interesting and useful operation between subsequent system versions is the model difference calculation and representation. In this paper, a metamodel independent approach to the representation of model differences which is agnostic of the calculation method is presented. Given two models which conform to a metamodel, their difference is conforming to another metamodel derived from the former by an automated transformation. Difference models are first-class entities which induce transformations able to apply the modifications they specify. Finally, difference models can be composed sequentially and in parallel giving place to more complex modifications.