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AI-Driven Predictive Modeling for Enhancing Software Quality, Maintainability, and Reusability in Object-Oriented Architecture and Component-Based Development

Author(s) Durga Prasad
Country United States
Abstract Artificial Intelligence (AI)-driven predictive modeling has emerged as a transformative approach for improving software quality attributes, particularly maintainability and reusability, in object-oriented architecture and component-based development. Traditional metric-based evaluation techniques provide static insights into system complexity; however, they often fail to predict long-term architectural sustainability and evolution. This research explores AI-enabled predictive frameworks that leverage machine learning algorithms, software metrics, and architectural analysis to proactively identify defects, estimate maintainability, optimize modularity, and enhance component reuse. By integrating predictive analytics within object-oriented and component-based systems, the study proposes a structured framework that combines code metrics, change-history mining, dependency modeling, and architectural visualization. The findings demonstrate that AI-based models significantly improve prediction accuracy for maintainability indices, refactoring needs, and component adaptability, thereby reducing technical debt and lifecycle cost. This research contributes a conceptual architecture, predictive workflow, and visualization models for AI-driven software quality enhancement.
Keywords AI-driven software engineering, predictive modeling, software quality, maintainability prediction, software reusability, object-oriented architecture, component-based development, machine learning in SE, software metrics, defect prediction.
Field Engineering
Published In Volume 6, Issue 8, August 2025
Published On 2025-08-07
DOI https://doi.org/10.70528/IJLRP.v6.i8.2037
Short DOI https://doi.org/hbtkvg
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