It can be difficult for developers to select the best solution for their projects due to the abundance of chatbot development platforms and frameworks. This paper explores the selection of frameworks and platforms for designing chatbots, based on criteria from numerous scientific articles. The introduction covers the axes and sections of the paper, including frameworks, platforms, metrics, and paper details. The second section reviews previous studies on the topic, examining frameworks and platforms used, metrics, and other details. An expansion of software-related services devoted to chatbot development has resulted from the necessity for these services to be produced in large quantities quickly and effectively. The third section examines the latest frameworks and platforms, various sources of articles and scientific research published in prestigious international databases. Large corporations compete with one another and offer comprehensive chatbot development platforms include Google, Microsoft, Amazon, and IBM. We also talk about chatbot platform and measures of evaluation framework while showcasing successful industrial practices. The fourth section proposes methodologies for choosing frameworks or platforms based on findings from numerous scientific research, master's and doctoral theseis, and important scientific books by prominent authors. The fifth section discusses the criteria for measuring chatbot efficiency and the best frameworks and platforms according to these metrics. Scholars, developers, and businesses are given recommendations that point to potential areas for further research and development in this rapidly evolving section. The final section presents the conclusions, listing details and section mentioned in the paper, and a list of references, including about a hundred references from prestigious scientific articles. This scientific paper provides individuals, groups, and large and small companies with mental and intellectual enlightenment, helping them make decisions on their chatbot designing by choosing the most appropriate frameworks and platforms.
Primary Language | English |
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Subjects | Software Engineering (Other) |
Journal Section | Articles |
Authors | |
Early Pub Date | July 18, 2024 |
Publication Date | July 1, 2024 |
Submission Date | January 16, 2024 |
Acceptance Date | April 7, 2024 |
Published in Issue | Year 2024 |