Design-Level Refactoring Using Genetic Algorithms in an Academic System - Priyambadha, Bayu; Santoso, Nurudin (International Journal of Informatics and Computation, 2025)
Legacy software systems undergo structural degradation due to continuous evolution, which makes maintenance increasingly complex. This study investigates the effectiveness of design-level refactoring using a genetic algorithm (GA)-based class decomposition method in a legacy academic information system of FILKOM. We utilize static analysis via CodeMR to detect code smells and evaluate maintainability using the Maintainability Index (MI). Our findings reveal a significant increase in median MI from 28.25 to 70.90 post-refactoring. While formal statistical significance was not reached (p = 0.10), the effect size was consistently strong (r = 1.00), confirming the positive impact of the proposed approach. The study contributes a practical, replicable method for improving maintainability in real-world systems, supported by semantic similarity and usability-based decomposition. This work highlights the potential of design-level optimization using evolutionary algorithms and calls for further multi-domain validation.