Positioning of the low-code movement within the field of model-driven engineering

Cabot, Jordi

doi:10.1145/3417990.3420210

Cited by 64 publications

(32 citation statements)

References 5 publications

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“…As model-driven engineering (MDE) [3] has similar aims, there is an ongoing debate on how low-code software development is different from model-driven engineering and to what extent work carried out in the field of MDE is directly transferable to LCDPs [6]. In this paper, we aim at clarifying the commonalities and differences between both approaches.…”

mentioning

confidence: 99%

Low-code development and model-driven engineering: Two sides of the same coin?

et al. 2022

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The last few years have witnessed a significant growth of so-called low-code development platforms (LCDPs) both in gaining traction on the market and attracting interest from academia. LCDPs are advertised as visual development platforms, typically running on the cloud, reducing the need for manual coding and also targeting non-professional programmers. Since LCDPs share many of the goals and features of model-driven engineering approaches, it is a common point of debate whether low-code is just a new buzzword for model-driven technologies, or whether the two terms refer to genuinely distinct approaches. To contribute to this discussion, in this expert-voice paper, we compare and contrast low-code and model-driven approaches, identifying their differences and commonalities, analysing their strong and weak points, and proposing directions for cross-pollination.

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mentioning

confidence: 99%

Low-code development and model-driven engineering: Two sides of the same coin?

et al. 2022

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“…These new type of UIs, which are hard to specify [37], test, verify and debug [39] and require a different and specialized skillset [25] could benefit as well from a model-based perspective. Right now, most of the initiatives focus on CUIs and, in particular, chatbots, mainly lead by commercial companies offering a kind of low-code/no-code [12] front-end to their specific chatbot platform. Some examples are: Tock 11 , Engati 12 or FlowXO 13 .…”

Section: Related Workmentioning

confidence: 99%

Towards a model-driven approach for multiexperience AI-based user interfaces

et al. 2021

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Software systems start to include other types of interfaces beyond the “traditional” Graphical-User Interfaces (GUIs). In particular, Conversational User Interfaces (CUIs) such as chat and voice are becoming more and more popular. These new types of interfaces embed smart natural language processing components to understand user requests and respond to them. To provide an integrated user experience all the user interfaces in the system should be aware of each other and be able to collaborate. This is what is known as a multiexperience User Interface. Despite their many benefits, multiexperience UIs are challenging to build. So far CUIs are created as standalone components using a platform-dependent set of libraries and technologies. This raises significant integration, evolution and maintenance issues. This paper explores the application of model-driven techniques to the development of software applications embedding a multiexperience User Interface. We will discuss how raising the abstraction level at which these interfaces are defined enables a faster development and a better deployment and integration of each interface with the rest of the software system and the other interfaces with whom it may need to collaborate. In particular, we propose a new Domain Specific Language (DSL) for specifying several types of CUIs and show how this DSL can be part of an integrated modeling environment able to describe the interactions between the modeled CUIs and the other models of the system (including the models of the GUI). We will use the standard Interaction Flow Modeling Language (IFML) as an example “host” language.

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“…Unlike other solutions, CA empowers process engineers to quickly apply AI data applications without requiring coding skills. The solution offers prebuilt templates to minimize coding for process engineers, using a Low-Code/No-Code approach [18].…”

Section: The Approach With Canvass Analytics Platformmentioning

confidence: 99%

New Business Models on Artificial Intelligence—The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

Redchuk

Mateo

2021

ASI

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This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution.

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Positioning of the low-code movement within the field of model-driven engineering

Cited by 64 publications

References 5 publications

Low-code development and model-driven engineering: Two sides of the same coin?

Low-code development and model-driven engineering: Two sides of the same coin?

Towards a model-driven approach for multiexperience AI-based user interfaces

New Business Models on Artificial Intelligence—The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

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