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

Juanda Hakim Lubis, Elviawaty Muisa Zamzami, Mahyuddin K. M Nasution, Mohammad Andri Budiman

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.


Keywords


Database Reverse Engineering, FCI–ANTREE, GUI Form, Schema Recovery, ER-Diagram, Legacy System

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References


F. Vandeputte, Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems, vol. 1, no. 1. arXiv, 2025.

Z. Van Veldhoven and J. Vanthienen, “Best practices for digital transformation based on a systematic literature review,” Digital Transformation and Society, vol. 2, no. 2, pp. 104–128, 2023, doi: 10.1108/DTS-11-2022-0057.

J. Díaz-Arancibia, J. Hochstetter-Diez, A. Bustamante-Mora, S. Sepúlveda-Cuevas, I. Albayay, and J. Arango-López, “Navigating Digital Transformation and Technology Adoption: A Literature Review from Small and Medium-Sized Enterprises in Developing Countries,” Sustainability, vol. 16, no. 14, 2024, doi: 10.3390/su16145946.

A. Rahman and P. Pingali, “Social Welfare `Schemes’ to an Economic Security `System’,” in The Future of India’s Social Safety Nets: Focus, Form, and Scope, Cham: Springer International Publishing, 2024, pp. 357–425. doi: 10.1007/978-3-031-50747-2_10.

A. Agbeyangi and H. Suleman, “Advances and Challenges in Low-Resource-Environment Software Systems: A Survey,” Informatics, vol. 11, no. 4, 2024, doi: 10.3390/informatics11040090.

L. E. Sánchez et al., “MARISMA: A modern and context-aware framework for assessing and managing information cybersecurity risks,” Computer Standards & Interfaces, vol. 92, p. 103935, 2025, doi: https://doi.org/10.1016/j.csi.2024.103935.

G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artificial Intelligence Review, vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.

C. Yang, Y. Liu, and C. Yin, “Recent Advances in Intelligent Source Code Generation: A Survey on Natural Language Based Studies,” Entropy, vol. 23, no. 9, 2021, doi: 10.3390/e23091174.

Y. Kumar, J. Marchena, A. H. Awlla, J. J. Li, and H. B. Abdalla, “The AI-Powered Evolution of Big Data,” Applied Sciences, vol. 14, no. 22, 2024, doi: 10.3390/app142210176.

H. Choi and S. Lee, “Forensic analysis of SQL server transaction log in unallocated area of file system,” Forensic Science International: Digital Investigation, vol. 46, p. 301605, 2023, doi: https://doi.org/10.1016/j.fsidi.2023.301605.

J. Shin, “Memory-Driven Forensic Analysis of SQL Server: A Buffer Pool and Page Inspection Approach,” Sensors, vol. 25, no. 11, 2025, doi: 10.3390/s25113512.

W. Dobrowolski, M. Nikodem, and O. Unold, “Software Failure Log Analysis for Engineers—Review,” Electronics, vol. 12, no. 10, 2023, doi: 10.3390/electronics12102260.

S. Mohammed et al., “The effects of data quality on machine learning performance on tabular data,” Information Systems, vol. 132, p. 102549, 2025, doi: https://doi.org/10.1016/j.is.2025.102549.

A. S. Abdelfattah, T. Cerny, J. Yero Salazar, X. Li, D. Taibi, and E. Song, “Assessing Evolution of Microservices Using Static Analysis,” Applied Sciences, vol. 14, no. 22, 2024, doi: 10.3390/app142210725.

M. Á. Rodríguez-Ortiz, P. C. Santana-Mancilla, and L. E. Anido-Rifón, “Machine Learning and Generative AI in Learning Analytics for Higher Education: A Systematic Review of Models, Trends, and Challenges,” Applied Sciences, vol. 15, no. 15, 2025, doi: 10.3390/app15158679.

E. Dritsas and M. Trigka, “Exploring the Intersection of Machine Learning and Big Data: A Survey,” Machine Learning and Knowledge Extraction, vol. 7, no. 1, 2025, doi: 10.3390/make7010013.

F. van der Sluis and E. L. van den Broek, “Model interpretability enhances domain generalization in the case of textual complexity modeling,” Patterns, vol. 6, no. 2, p. 101177, 2025, doi: https://doi.org/10.1016/j.patter.2025.101177.

F. Li and H. V Jagadish, “Constructing an interactive natural language interface for relational databases,” Proc. VLDB Endow., vol. 8, no. 1, pp. 73–84, Sep. 2014, doi: 10.14778/2735461.2735468.

F. Babič et al., “Review of Tools for Semantics Extraction: Application in Tsunami Research Domain,” Information, vol. 13, no. 1, 2022, doi: 10.3390/info13010004.

Z. Brahmia, F. Grandi, and B. Oliboni, “A Literature Review on Schema Evolution in Databases,” Computing Open, vol. 02, p. 2430001, 2024, doi: 10.1142/S2972370124300012.

T. I. Mohottige, A. Polyvyanyy, C. Fidge, R. Buyya, and A. Barros, “Reengineering software systems into microservices: State-of-the-art and future directions,” Information and Software Technology, vol. 183, p. 107732, 2025, doi: https://doi.org/10.1016/j.infsof.2025.107732.

M. Chen, T. S. Martins, L. Zhang, and H. Dong, “Digital Transformation in Project Management: A Systematic Review and Research Agenda,” Systems, vol. 13, no. 8, 2025, doi: 10.3390/systems13080625.

G. Becce, L. Mariani, O. Riganelli, and M. Santoro, “Extracting Widget Descriptions from GUIs,” pp. 347–361, 2012.

G. Costagliola, M. De Rosa, V. Fuccella, and M. Minas, “Visual exploration of visual parser execution,” Multimedia Tools and Applications, vol. 81, no. 1, pp. 299–317, 2022, doi: 10.1007/s11042-021-10624-6.

T. Theunissen, U. van Heesch, and P. Avgeriou, “A mapping study on documentation in Continuous Software Development,” Information and Software Technology, vol. 142, p. 106733, 2022, doi: https://doi.org/10.1016/j.infsof.2021.106733.

J. Cheng, J. Zhao, W. Xu, T. Zhang, F. Xue, and S. Liu, “Semantic Similarity-Based Mobile Application Isomorphic Graphical User Interface Identification,” Mathematics, vol. 11, no. 3, 2023, doi: 10.3390/math11030527.

J. Spinak, “Model-based GUI automation,” Software and Systems Modeling, 2025, doi: 10.1007/s10270-025-01319-9.

S. Choi and Y. Jung, “Knowledge Graph Construction: Extraction, Learning, and Evaluation,” Applied Sciences, vol. 15, no. 7, 2025, doi: 10.3390/app15073727.

L. Abb and J.-R. Rehse, “Process-related user interaction logs: State of the art, reference model, and object-centric implementation,” Information Systems, vol. 124, p. 102386, 2024, doi: https://doi.org/10.1016/j.is.2024.102386.

A. Kousar, S. U. R. Khan, A. Mashkoor, and J. Iqbal, “A Systematic Literature Review on Graphical User Interface Testing Through Software Patterns,” IET Software, vol. 2025, no. 1, p. 9140693, 2025, doi: https://doi.org/10.1049/sfw2/9140693.

Y. Chen, G. Chen, and P. Li, “Named Entity Recognition in Track Circuits Based on Multi-Granularity Fusion and Multi-Scale Retention Mechanism,” Electronics, vol. 14, no. 5, 2025, doi: 10.3390/electronics14050828.

J. Wang, “Multi-Modal Topology-Aware Graph Neural Network for Robust Chemical–Protein Interaction Prediction,” International Journal of Molecular Sciences, vol. 26, no. 17, 2025, doi: 10.3390/ijms26178666.

H. Xia, M. Liu, P. Wang, and X. Tan, “Strategies to enhance the corporate innovation resilience in digital era: A cross-organizational collaboration perspective,” Heliyon, vol. 10, no. 20, p. e39132, 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e39132.

N. Maksoud, H. AlJassmi, L. Ali, and A. R. Masoud, “Applications of large language models and generative AI in transportation: A systematic review and bibliometric analysis,” Transportation Research Interdisciplinary Perspectives, vol. 34, p. 101699, 2025, doi: https://doi.org/10.1016/j.trip.2025.101699.

D. A. Patil and S. G., “A comprehensive survey on securing the social internet of things: protocols, threat mitigation, technological integrations, tools, and performance metrics,” Scientific Reports, vol. 15, no. 1, p. 40190, 2025, doi: 10.1038/s41598-025-23865-4.

H. I. Aysel, X. Cai, and A. Prugel-Bennett, “Explainable Artificial Intelligence: Advancements and Limitations,” Applied Sciences, vol. 15, no. 13, 2025, doi: 10.3390/app15137261.

W. Yang et al., “Survey on Explainable AI: From Approaches, Limitations and Applications Aspects,” Human-Centric Intelligent Systems, vol. 3, no. 3, pp. 161–188, 2023, doi: 10.1007/s44230-023-00038-y.

A. M. Memon, “Using Reverse Engineering for Automated Usability Evaluation of Gui-Based Applications,” in Human-Centered Software Engineering: Software Engineering Models, Patterns and Architectures for HCI, A. Seffah, J. Vanderdonckt, and M. C. Desmarais, Eds., London: Springer London, 2009, pp. 335–355. doi: 10.1007/978-1-84800-907-3_16.

I. C. Morgado, A. C. R. Paiva, and J. P. Faria, “Dynamic Reverse Engineering of Graphical User Interfaces,” International Journal on Advances in Software, vol. 5, no. 3, pp. 224–236, 2012.

J. C. Campos, J. Saraiva, C. Silva, and J. C. Silva, “GUIsurfer : A Reverse Engineering Framework for User Interface Software,” 2003.

M. Lungu, M. Lanza, T. Gîrba, and R. Robbes, “The Small Project Observatory: Visualizing software ecosystems,” Science of Computer Programming, vol. 75, no. 4, pp. 264–275, 2010, doi: https://doi.org/10.1016/j.scico.2009.09.004.

C. Sacramento and A. C. R. Paiva, “Web Application Model Generation through Reverse Engineering and UI Pattern Inferring,” in 2014 9th International Conference on the Quality of Information and Communications Technology, 2014, pp. 105–115. doi: 10.1109/QUATIC.2014.20.

P. Aho, T. Räty, and N. Menz, “Dynamic reverse engineering of GUI models for testing,” in 2013 International Conference on Control, Decision and Information Technologies (CoDIT), 2013, pp. 441–447. doi: 10.1109/CoDIT.2013.6689585.

H. Bünder and H. Kuchen, “A model-driven approach for behavior-driven GUI testing,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, in SAC ’19. New York, NY, USA: Association for Computing Machinery, 2019, pp. 1742–1751. doi: 10.1145/3297280.3297450.

M. Zanoni, F. Perin, F. A. Fontana, and G. Viscusi, “Pattern detection for conceptual schema recovery in data-intensive systems,” Journal of Software: Evolution and Process, vol. 26, no. 12, pp. 1172–1192, 2014, doi: https://doi.org/10.1002/smr.1656.

A. Mparmpoutis and G. Kakarontzas, “Using Database Schemas of Legacy Applications for Microservices Identification: A Mapping Study,” in Proceedings of the 6th International Conference on Algorithms, Computing and Systems, in ICACS ’22. New York, NY, USA: Association for Computing Machinery, 2023. doi: 10.1145/3564982.3564995.

H. Sneed and C. Verhoef, “Re-implementing a legacy system,” Journal of Systems and Software, vol. 155, pp. 162–184, 2019, doi: https://doi.org/10.1016/j.jss.2019.05.012.

C.-F. Yu, J.-W. Peng, C.-C. Hsiao, C.-H. Wang, and W.-C. Lo, “Development of GUI-Driven AI Deep Learning Platform for Predicting Warpage Behavior of Fan-Out Wafer-Level Packaging,” Micromachines, vol. 16, no. 3, 2025, doi: 10.3390/mi16030342.

B. Althani, “Migration challenges of legacy software to the cloud: a socio-technical perspective,” Cogent Business & Management, vol. 12, no. 1, p. 2503421, 2025, doi: 10.1080/23311975.2025.2503421.

I. Harsh, M. C. Patel, and R. Gururaj, “Reverse Engineering Database to ER Model,” in 2025 6th International Conference on Recent Advances in Information Technology (RAIT), 2025, pp. 1–6. doi: 10.1109/RAIT65068.2025.11089060.

B. Paradauskas and A. Laurikaitis, “Extracting Conceptual Data Specifications from Legacy Information Systems,” Electronics And Electrical Engineering, vol. 1, no. 1, pp. 46–50, 2011.

ShinKwang-chul and L. Y., “Database Reverse Engineering Using Master Data in Microservice Architecture,” Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 5, pp. 523–532.

D. Yeh, Y. Li, and W. Chu, “Extracting entity-relationship diagram from a table-based legacy database,” Journal of Systems and Software, vol. 81, no. 5, pp. 764–771, 2008, doi: https://doi.org/10.1016/j.jss.2007.07.005.




DOI: https://doi.org/10.47738/jads.v7i3.1383

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