| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 13 - Number 5 |
| Year of Publication: 2026 |
| Authors: Olena Komleva |
10.5120/ijais3d50704b9a44
|
Olena Komleva . Methodology for Developing and Optimizing Web Applications Based on Java and JavaScript Technologies. International Journal of Applied Information Systems. 13, 5 ( Oct 2026), 6-13. DOI=10.5120/ijais3d50704b9a44
Purpose. This article develops an integrated methodology for building and optimizing web applications with Java and JavaScript, treating architecture, testing, runtime analysis, and iterative refinement as parts of a single engineering cycle rather than as isolated tasks. Methodology. The study employs a qualitative meta-synthesis design that aggregates empirical evidence from heterogeneous sources. Twenty verified English-language publications were synthesized, including systematic reviews, empirical software engineering papers, industrial case studies, and foundational works on Java, JavaScript, testing, and performance. A compact secondary analytical layer compares recurring optimization factors, testing practices, and quality indicators across the selected studies. Findings. Sustainable performance gains emerge when server-side decisions, client-side behavior, observability, and automated testing are aligned early in the lifecycle. Bottlenecks in web systems rarely originate in one layer. They are usually produced by interactions among backend configuration, frontend execution paths, dependency behavior, and incomplete testing coverage. A profile of the corpus by period and venue and an application to three project scenarios extend the synthesis. Unique contribution to theory, policy, and practice. The article proposes an authorial practical framework, the Integrated Dual-Layer Optimization Cycle, which translates empirical findings into a replicable workflow for Java and JavaScript web projects. The framework addresses researchers, educators, and practitioners who need a methodologically coherent way to connect development choices with maintainability, scalability, and measurable optimization outcomes.