Research Article

Role of Low-Code/No-Code Platforms in Engineering Software Development: Democratizing Simulation and Design

Authors

  • Sarmi Islam Independent Researcher, Eden Mohila College, Dhaka

Abstract

The momentum of lowcode and no-code (LCNC) platforms has ushered a new era in engineering software development, empowering non-programmer engineers, designers and domain experts to contribute to simulation, automation and product innovation. In this work we explore how LCNC platforms drive engineering design democratization by reducing technical barriers, shortening development cycles, and stimulating interdisciplinary collaboration. Using systematic literature review and conceptual analysis methods, its aim is to investigate the transformative potential of LCNC tools for encapsulating simulation, computational modelling and AI-based workflows in accessible, visual interfaces. And it indicates how these platforms foster productivity growth, diminish the need for specialised programming expertise and facilitate inclusive innovation at scale among both small- and large-scale businesses. However, the paper also presents challenges that are emerging like model validation scavenging and data interoperability and potential risks of governance in specific to safety critical engineering domains. The results highlight the potential for LCNC methods to supplement—not substitute—conventional programming idioms with appropriate design guidelines, domain specific templates and responsible automation. At the end, in this paper we argue for a human-centered LCNC ecosystem that enables engineers to concentrate on creativity and problem solving, while ensuring transparency, accountability, and technical integrity in engineering software development.

Article information

Journal

Frontiers in Computer Science and Artificial Intelligence

Volume (Issue)

1 (1)

Pages

26-39

Published

2024-11-25

How to Cite

Role of Low-Code/No-Code Platforms in Engineering Software Development: Democratizing Simulation and Design. (2024). Frontiers in Computer Science and Artificial Intelligence, 1(1), 26-39. https://al-kindipublisher.com/index.php/fcsai/article/view/11442

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Keywords:

AI-Driven Cybersecurity, Intrusion Detection Systems, Adversarial Machine Learning, Explainable Artificial Intelligence (XAI)