Model-Driven Incremental Modernization of a Django–MySQL Monolithic English Test into Go–PostgreSQL-ReactJS Microservices
- Iqbal Fadhil Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
-
Umi Laili Yuhana
*
Institut Teknologi Sepuluh Nopember, Surabaya,
Indonesia
https://orcid.org/0000-0001-5237-7457
- Sarwosri Sarwosri Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia https://orcid.org/0009-0004-5779-4814 (*) Corresponding Author
Abstract
The need for efficient and scalable web-based learning systems is increasing as English proficiency becomes a key employability factor worldwide. The existing English qualification try-out platform, built as a Django–MySQL monolith, showed inadequate performance under concurrent real-exam workloads. This study addresses these limitations by migrating the platform to a Go–PostgreSQL–ReactJS microservices architecture using the Model-Driven Incremental Modernization (MDIM) methodology. The migration starts with reverse engineering to extract structural and behavioral models, represented as UML component and sequence diagrams that capture dependencies and execution flows. These models guide service boundary identification, transformation planning, and stepwise service extraction and validation. Therefore, authentication, user management, and examination workflows are incrementally decomposed into independently deployable services while preserving functional correctness. The new system is deployed as native processes on Ubuntu Server 22.04 and evaluated with k6 for API load testing, JMeter for end-to-end scenarios, and Python scripts for resource and database performance monitoring. Experimental results show that the microservices system reaches 127.57 requests per second with 156.97 ms average latency and 342.68 ms P95 latency, while the monolith on Ubuntu handles 33.40 requests per second and the monolith on cPanel 28.69 requests per second with much higher latency and CPU utilization. These findings demonstrate that the MDIM-guided microservices migration improves scalability, responsiveness, and resource efficiency and provides a reusable, model-based reference for modernizing similar educational assessment platforms.
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Copyright (c) 2026 Iqbal Fadhil, Umi Laili Yuhana, Sarwosri Sarwosri
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