Effort Estimation for Complex Business Logic Migration in Legacy Systems: A Case Study of Hospital Information System Modules in Ukraine

The object of research is the migration of a legacy hospital information system to a Domain-Driven Design architecture. The problem is accurate effort estimation under changing conditions, since use case-based methods rely on global, project-wide adjustment factors that are too abstract to capture localized architectural, logical, and technological anomalies within individual functional blocks. A modified Use Case Size Points method is proposed, integrating a multidimensional Complexity Amplification Factor across the Infrastructure, Logic, and Technology domains, each scored on calibrated indicator scales. The method was calibrated and validated on real functional blocks of the Unified Clinico-Statistical Classification of Disease subsystems of a Ukrainian surgical clinic. Incorporating the Complexity Amplification Factor increased the accuracy of prediction for query-oriented tasks by approximately 7.6 times (mean relative estimation error reduced from 120.0% to 15.8%), and for command-oriented tasks by approximately 3.6 times (mean relative estimation error reduced from 57.2% to 15.7%). In both cases, the resulting error falls within the range generally considered acceptable for reliable project planning, whereas the baseline method's errors were large enough to cause systematic effort underestimation and budget overruns. This improvement occurs because the Complexity Amplification Factor captures non-standard business logic and hidden infrastructural and technological effort that purely structural size metrics overlook. As a result, project managers obtain a more realistic and trustworthy basis for scheduling, cost control, and risk assessment when planning similar legacy migration projects.The method applies to effort estimation, planning, budgeting, and auditing of legacy migration projects, provided the Complexity Amplification Factor weights and the man-hour coefficient are recalibrated per task class and organizational context.