Analysis of Low Code-No Code Development Platforms in comparison with Traditional Development Methodologies

doi:10.22214/ijraset.2021.39328

Paper

Analysis of Low Code-No Code Development Platforms in comparison with Traditional Development Methodologies

Published Dec 31, 2021 · Shreyas Shridhar

International Journal for Research in Applied Science and Engineering Technology

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Abstract

Abstract: This paper examines the overview of low-code/no-code development platforms in comparison with traditional development methodologies and examines the benefits and limitations of the same. For several decades, businesses have had multiple options when they demanded new information systems. They could develop a new system using in-house developers, or they could order a system from an external merchant. This offers a close fit to business obligations. However, nowadays, there is a new alternative that is becoming increasingly prevalent. Low code/no code (LC/NC) applications can cater to business requirements efficiently, can be implemented instantly, and the cost is much less than systems developed in-house. Few, if any, programming skills are required. Keywords: Traditional development, No code development, low code development, Low code No code development, Software development life cycle (SDLC)

Study Snapshot

Low-code/no-code development platforms offer efficient, cost-effective, and instant application implementation, while requiring minimal programming skills compared to traditional development methods.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Paper

Analysis of Low Code-No Code Development Platforms in comparison with Traditional Development Methodologies

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References

Citations

Making Sense of Developing Artificial Intelligence-Based System in Software Development Life Cycle Manner and Addressing Risk Factors

This paper proposes a model for AI-SDLC, integrating the waterfall model and risk factors, to enhance the success of AI-based systems in software development life cycle.