Experimental Data and Python Code for Priority Recovery of Critical Components in a Mobile Platform-Based Information System

This record contains the Python implementation, model-generated experimental data, summary tables, and figures supporting the study “A Method for Priority Recovery of Critical Components of a Mobile Platform-Based Information System Using Decomposition of Their Contributions to the Increase in the Integral Survivability Index”. The experiment compares four recovery-prioritization methods under additive, overlap, synergy, and mixed component-interaction scenarios. The main experiment includes nine critical components, 100 realizations per scenario, and available recovery budgets equal to 25%, 40%, and 55% of the total recovery cost. The exact optimum for each generated instance and budget is determined by exhaustive enumeration and used as a reference. A separate experiment evaluates the reduction in the number of subsets considered when the decomposition is localized to connected subsets of the active dependency graph. The accompanying Python script reproduces all CSV tables and PNG figures using the fixed random seed 20260715. The deposited data are synthetic results of a reproducible model experiment. They are not measurements from a deployed information system and contain no personal or confidential information.