A Metascience Study of the Impact of Low-Code Techniques in Modeling Publications

2024-08-12

arXiv.org

AI Summary

The academic positioning of low-code development relative to classical model-driven development remains ambiguous, and the relationship between their respective research communities lacks systematic clarification. Method: This paper conducts the first meta-scientific study, integrating bibliometric analysis, author-venue-topic network modeling, and cross-community comparative analysis to quantitatively characterize the low-code community’s scale, disciplinary diversity, publication venue distribution, and scholarly output characteristics—and to systematically compare them with those of the classical model-driven development community. Contribution/Results: We find that the low-code community exhibits strong interdisciplinarity and conference-centric publication patterns, and has significantly diverged from traditional modeling communities. These findings provide empirical grounding for conceptualizing low-code as an independent research trajectory, reveal opportunities for disciplinary integration, and identify critical interfaces for collaborative innovation—thereby informing the reconfiguration and convergence of the broader modeling research community.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures

Application Category

• Web Mining and Content Analysis: Community question answering
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

In the last years, model-related publications have been exploring the application of modeling techniques in different domains. Initially focused on UML and the Model-Driven Architecture approach, the literature has been evolving towards the usage of more general concepts such as Model-Driven Development or Model-Driven Engineering. With the emergence of Low-Code software development platforms, the modeling community has been studying how these two fields may combine and benefit from each other, thus leading to the publication of a number of works in recent years. In this paper, we present a metascience study of Low-Code. Our study has a two-fold approach: (1) to examine the composition (size and diversity) of the emerging Low-Code community; and (2) to investigate how this community differs from the"classical"model-driven community in terms of people, venues, and types of publications. Through this study, we aim to benefit the low-code community by helping them better understand its relationship with the broader modeling community. Ultimately, we hope to trigger a discussion about the current and possible future evolution of the low-code community as part of its consolidation as a new research field.

Problem

• Analyzing composition and growth of Low-Code community
• Comparing Low-Code with classical model-driven approaches
• Exploring future collaboration between Low-Code and modeling fields

Innovation

• Metascience study of Low-Code field
• Analyze Low-Code community growth and composition
• Compare Low-Code with classical model-driven approaches