Modern Software Engineering Optimization Using Advanced Technologies
DOI:
https://doi.org/10.5281/zenodo.15481849Keywords:
software engineering, artificial intelligence, machine learning, web development, adaptive testingAbstract
This research investigates the optimization of modern software engineering through advanced technologies such as artificial intelligence (AI), machine learning (ML), and innovative web frameworks. The study demonstrates how AI and ML-based tools enhance resource forecasting and project management, while modern frameworks like React, MongoDB, and Jenkins streamline development and deployment. Adaptive testing with embedded AI improves software quality and user experience. Additionally, WebAssembly and blockchain ensure high-performance and secure front-end systems. The findings confirm that these technologies collectively increase efficiency, reliability, and security in software development.
References
Drofa, D. (2023). Integrate Bio-Identification to Strengthen Data Protection in Multi-Tenant Cloud Systems. In Global Innovations and Collaborative Solutions in Contemporary Science (pp. 444–447). Futurity Research Publishing. https://futurity-publishing.com/wp-content/uploads/2025/03/International_scientific_conference-.pdf#page=444
Drofa, D. (2024). Using Machine Learning to Forecast Resources in Long-Term Business Projects. In Horizons of Innovation: Conference on Multidisciplinary Trends in Science 2024 (pp. 395–398). Futurity Research Publishing. https://futurity-publishing.com/wp-content/uploads/2025/03/Drofa-D.-2024.pdf
Drofa, D. (2025). 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. (2025). 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
Horbenko, Y. (2025). 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. (2025). 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
Hunko, I. (2025). Adaptive Approaches to Software Testing with Embedded Artificial Intelligence in Dynamic Environments. International Journal of Current Science Research and Review, 8(5). https://doi.org/10.47191/ijcsrr/V8-i5-10
Hunko, I. (2025). 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
Ivanchenko, K. (2022). The Role of Adaptive Learning in the Training of Electronics and Automation Engineers. Futurity Education, 2(1), 86–105. https://doi.org/10.57125/FED.2022.25.03.8
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), 35–40. https://cyberleninka.ru/article/n/printsipy-postroeniya-raspredelennyh-globalnyh-faylovyh-hranilisch-s-obespecheniem-rezervirovaniya-dannyh-dlya-chteniya-i-vysokoy
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Vitalii Melnyk
This work is licensed under a Creative Commons Attribution 4.0 International License.