FCI-ANTREE: A GUI-Centric Method for Schema Recovery and Conceptual Database Model Reconstruction in Legacy Form-Based Systems

Abstract

Legacy systems that lack technical documentation present significant challenges for database schema recovery, particularly when access to source code and SQL queries is unavailable. Existing reverse engineering approaches predominantly rely on backend artifacts such as database logs, schema definitions, or program code, limiting their applicability in undocumented environments. Although GUI-based approaches offer an alternative by utilizing interface-level information, many existing methods still rely on shallow visual parsing and lack systematic mechanisms to capture structural and interaction semantics. To address this limitation, this study proposes FCI–ANTREE, a GUI-centric method for reconstructing conceptual database schemas from legacy form-based systems. The method integrates Form-Centric Interaction (FCI) to extract candidate entities, attributes, relationships, constraints, and data types from user interactions and validation logic, and Admin Interface Tree (ANTREE) to model hierarchical relationships and transform them into logical and relational schemas. The objective of this research is to provide a systematic, interpretable, and semi-automated approach for database reverse engineering without relying on backend access. The proposed method was evaluated using three case studies: an online store application, a library system, and an inventory application. The evaluation employed structural consistency analysis, confusion-matrix-based metrics, and quantitative error measurements. The results show that the method achieved a mean MAE of 1.77, RMSE of 3.16, and R² of 97.09%, along with an average F1-score of 0.8768, indicating a high level of agreement between reconstructed and reference schemas. These findings demonstrate that FCI–ANTREE provides an effective and practical solution for database schema reconstruction in legacy systems with limited or no backend accessibility. The method contributes by introducing an interaction-aware and rule-based framework that enhances the accuracy, interpretability, and applicability of GUI-driven reverse engineering.