Optimization of Full-Stack Web Development through Modern Technology Stacks
DOI:
https://doi.org/10.5281/zenodo.15518538Keywords:
optimization, full cycle, web development, technology stack, productivityAbstract
This paper explores the optimization of the full-cycle web development process using modern technology stacks, including React, MongoDB, Jenkins, and WebAssembly. By analyzing deployment efficiency, testing automation, and API integration, the study reveals key productivity and security improvements. AI-based forecasting and confidential computing further enhance system scalability and protection. The results support strategic technology stack selection for higher-performance web services.
References
Drofa, D. (2025a). Optimizing Web Development and Deployment Efficiency: The Impact of React, MongoDB, and Jenkins in Modern Software Engineering. Asian Journal of Research in Computer Science, 18(4), 237–255. https://doi.org/10.9734/ajrcos/2025/v18i4617
Drofa, D. (2025b). Using Artificial Intelligence for Resource Forecasting in Strategic Project Management. Asian Journal of Research in Computer Science, 18(5), 293–302. https://doi.org/10.9734/ajrcos/2025/v18i5656
Drofa, D. (2023). Integrate Bio-Identification to Strengthen Data Protection in Multi-Tenant Cloud Systems. Global Innovations and Collaborative Solutions in Contemporary Science, 444–447. https://futurity-publishing.com/wp-content/uploads/2025/03/International_scientific_conference-.pdf#page=444
Drofa, D. (2025c). Integrating Advanced API Solutions into Full-Stack Web and Mobile Applications to Optimise User Experience. International Journal of Current Science Research and Review, 08(05). https://doi.org/10.47191/ijcsrr/V8-i5-16
Drofa, D. (2025d). Optimization of software development processes through the use of full-stack technologies and automation. Contemporary Issues in Artificial Intelligence, 1. https://doi.org/10.69635/ciai.2025.12
Hunko, I. (2025a). Adaptive Approaches to Software Testing with Embedded Artificial Intelligence in Dynamic Environments. International Journal of Current Science Research and Review, 08(05). https://doi.org/10.47191/ijcsrr/V8-i5-10
Hunko, I. (2025b). Optimize Mobile App Testing Using Machine Learning to Improve User Experience. Asian Journal of Research in Computer Science, 18(5), 403–418. https://doi.org/10.9734/ajrcos/2025/v18i5663
Horbenko, Y. (2025a). Web Assembly and Blockchain for High-Performance Secure Front-End Systems. International Journal of Current Science Research and Review, 8(5), 2279–2285. https://doi.org/10.47191/ijcsrr/V8-i5-36
Horbenko, Y. (2025b). Confidential Computing in Front-End: Enhancing Data Security with Secure Enclaves and Homomorphic Encryption. International Journal of Advanced Multidisciplinary Research and Studies, 5(3), 308–321. https://www.multiresearchjournal.com/admin/uploads/archives/archive-1747130538.pdf
Голенев А.В. (2025). Принципы построения распределенных глобальных файловых хранилищ с обеспечением резервирования данных для чтения и высокой доступности хранения. Universum: технические науки, 1(130). https://cyberleninka.ru/article/n/printsipy-postroeniya-raspredelennyh-globalnyh-faylovyh-hranilisch-s-obespecheniem-rezervirovaniya-dannyh-dlya-chteniya-i-vysokoy/viewer
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Copyright (c) 2025 Chugaister Ilona
This work is licensed under a Creative Commons Attribution 4.0 International License.