FORMALIZED METHODOLOGY FOR COMPATIBILITY AND ADAPTATION OF REQUIREMENTS IN INTELLIGENT DIAGNOSTIC SYSTEMS
DOI:
https://doi.org/10.15588/1607-3274-2025-4-9Keywords:
methodology, software engineering, requirements engineering, requirements compatibility, scenario-based modeling, priority-based harmonizationAbstract
Context. Ensuring the consistency and adaptability of requirements in systems operating under dynamic conditions and limited resources is a pressing issue in modern requirements engineering, especially in intelligent diagnostic and decision-making environments. These systems must process conflicting, outdated, or ambiguous requirements while operating in environments characterized by high uncertainty and dynamic conditions.
Objective. This work introduces a formalized methodology for analyzing and managing the compatibility of system requirements. The proposed approach integrates logical consistency, functional interaction, resource feasibility, and priority alignment to support system stability and responsiveness.
Method. The methodology is implemented as a multi-level framework that incorporates formal representations of functional,
non-functional, and data-related requirements. It employs scenario-based modeling, a set of compatibility assessment models, and a dynamic algorithm for integrating new requirements. The integration process includes compatibility checks, adaptive refinement, expert-based weighting, and real-time feedback. The methodology’s applicability is demonstrated through a hypothetical intelligent medical diagnostic system.
Results. The proposed methodology enables systematic identification and resolution of requirement conflicts, ensuring consistent execution and effective prioritization under resource constraints. Scenario-driven modeling and the formalization of core requirements establish a foundation for adaptive system behavior and real-time decision-making.
Conclusions. The developed methodology, which includes models and algorithms, enhances the reliability of intelligent systems operating in critical contexts. Future work will focus on extending the framework by incorporating fuzzy logic, machine learning techniques, and developing software tools for automated compatibility analysis and adaptive requirements management.
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