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

Ruscio, Davide Di; Kolovos, Dimitrios S.; Lara, Juan de; Pierantonio, Alfonso; Tisi, Massimo; Wimmer, Manuel

doi:10.1007/s10270-021-00970-2

Cited by 74 publications

(36 citation statements)

References 18 publications

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“…Data processing and spreadsheets tools permit integrating data sources, thereby enabling for example the processing of newly appearing blockchain transactions, filtering for specified criteria, and calculations such as the aggregation of transferred amounts. Examples where this is possible are the spreadsheet tool AirTable (24) and data analytics tools such as Parabola (10).…”

Section: Resultsmentioning

confidence: 99%

“…These development platforms are typically available as cloud services with visual, diagrammatic interfaces and declarative languages. In our view, they constitute the next step in the industry adoption of academic model-driven engineering (MDE) approaches and its predecessors where models are regarded as primary development artifacts for software engineering [36,5,10]. While lowcode approaches allow a user to produce results without having to understand source code and there may be an underlying model integrated with features of the platform [4], the model may not conform to an explicit formalization [10].…”

Section: Introductionmentioning

confidence: 99%

“…In our view, they constitute the next step in the industry adoption of academic model-driven engineering (MDE) approaches and its predecessors where models are regarded as primary development artifacts for software engineering [36,5,10]. While lowcode approaches allow a user to produce results without having to understand source code and there may be an underlying model integrated with features of the platform [4], the model may not conform to an explicit formalization [10]. Further, we consider so-called no-code approaches as a subset of low-code approaches that operate at an abstraction level above code, not showing code to the user at all.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Blockchain Application Development Using Model-Driven Engineering and Low-Code Platforms: A Survey

Curty¹,

Härer²,

Fill³

2022

Preprint

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The creation of blockchain-based software applications requires today considerable technical knowledge, particularly in software design and programming. This is regarded as a major barrier in adopting this technology in business and making it accessible to a wider audience. As a solution, no-code and low-code approaches have been proposed that require only little or no programming knowledge for creating full-fledged software applications. In this paper we review academic approaches from the discipline of model-driven engineering as well as industrial no-code and low-code development platforms for blockchains. We further present a case study for an integrated no-code blockchain environment for demonstrating the state-of-the-art in this area. Based on the gained insights we derive requirements for the future development of no-code and low-code approaches that are dedicated to the field of blockchains.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Blockchain Application Development Using Model-Driven Engineering and Low-Code Platforms: A Survey

Curty¹,

Härer²,

Fill³

2022

Preprint

View full text Add to dashboard Cite

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“…Over the years, modeling has precisely defined a set of core terminology (model, metamodel, DSL, transformation, etc.). New terms are then contextualized in terms of the existing ones (e.g., the positioning of low-code [11,25]).…”

Section: Disciplines Have a Body Of Accumulated Specialist Knowl-mentioning

confidence: 99%

Modeling should be an independent scientific discipline

Cabot

Vallecillo

2022

Softw Syst Model

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Software modeling started as a paradigm to help developers build better software faster by enabling them to specify, reason and manipulate software systems at a higher-abstraction level while ignoring irrelevant low-level technical details. But this same principle manifests in any other domain that has to deal with complex systems, software-based or not. We argue that bringing to other engineering and scientific fields, our modeling expertise is a win–win opportunity where we can all learn from each other as we all model, but in complementary ways. Nevertheless, to fully unleash the benefits of this collaboration, we must go beyond individual efforts trying to adapt single techniques from one field to another. It requires a deeper reformulation of modeling as a whole. It is time for modeling to become an independent discipline where all fields of knowledge can contribute and benefit from.

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“…Although both Bock & Frank [19] and Luo et al [156] argue that there are no radical innovations in LCPs, Cabot [28] sees LCPs as an opportunity to bring existing knowledge and techniques under attention and use the new momentum to further the scientific knowledge. Ruscio et al [213] do see differences between MDE and LCPs such as platform (cloud versus desktop), users (citizen developers versus professional developers), and domain (business applications versus more technical domains).…”

Section: Michiel 1 Introductionmentioning

confidence: 99%

Evolution of Low-Code Platforms

Overeem¹

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Event sourced systems are increasing in popularity because they are reliable, flexible, and scalable. In this article, we point a microscope at a software architecture pattern that is rapidly gaining popularity in industry, but has not received as much attention from the scientific community. We do so through constructivist grounded theory, which proves a suitable qualitative method for extracting architectural knowledge from practitioners. Based on the discussion of 19 event sourced systems we explore the rationale for and the context of the event sourcing pattern. A description of the pattern itself and its relation to other patterns as discussed with practitioners is given. The description itself is grounded in the experience of 25 engineers, making it a reliable source for both new practitioners and scientists. We identify five challenges that practitioners experience: event system evolution, the steep learning curve, lack of available technology, rebuilding projections, and data privacy. For the first challenge of event system evolution, we uncover five tactics and solutions that support practitioners in their design choices when developing evolving event sourced systems: versioned events, weak schema, upcasting, in-place transformation, and copy-and-transform.

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

Cited by 74 publications

References 18 publications

Blockchain Application Development Using Model-Driven Engineering and Low-Code Platforms: A Survey

Blockchain Application Development Using Model-Driven Engineering and Low-Code Platforms: A Survey

Modeling should be an independent scientific discipline

Evolution of Low-Code Platforms

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