Low-Code Platforms for Rapid Prototyping in Enterprise Applications
DOI:
https://doi.org/10.63345/Keywords:
low-code, rapid prototyping, enterprise applications, model-driven engineering, DevOps, governance, usability, time-to-valueAbstract
Enterprises increasingly need to validate ideas quickly while minimizing risk and cost. Low-code development platforms (LCDPs) promise rapid prototyping by combining visual modeling, reusable components, and model-driven automation with enterprise-grade connectors and governance. This manuscript investigates whether—and under what conditions—low-code accelerates early-stage enterprise application delivery without undermining quality, security, or maintainability. We synthesize prior work on rapid application development, model-driven engineering, and platform governance, and present a mixed-methods study comprising a controlled build-off (6 low-code teams vs. 6 code-first teams), practitioner interviews, and a simulation of portfolio-level throughput under varying complexity and rework rates. Quantitative results indicate that low-code teams produced a functional prototype 30.4% faster (mean 7.8 vs. 11.2 days), reduced early defect density by 38.7%, and closed change requests 40.5% faster, while achieving higher usability scores (SUS +10.1%).
Qualitative findings emphasize that gains are largest for CRUD-heavy workflows with standard integrations and clear UX patterns; benefits taper for compute-intensive logic, highly bespoke UI, or edge-case integrations where custom code dominates. The simulation suggests portfolio-level cycle-time improvements of 23–36% when rework rates are moderated through frequent stakeholder validation—an affordance of visual modeling and instant previews in low-code. We discuss governance guardrails (versioning, policy-as-code, access control), architectural patterns (strangler fig, event gateways), and DevOps integration strategies that preserve speed while safeguarding compliance. We conclude with a decision framework aligning platform features with enterprise constraints, and we outline limitations and directions for future research on scaling patterns and technical debt forecasting in low-code ecosystems.
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Articles are published under the Creative Commons Attribution NonCommercial 4.0 License (CC BY NC 4.0), allowing others to distribute, remix, adapt, and build upon the work for non-commercial purposes while crediting the original author.






