Analyze the Impact of Technical Debt Prediction Models on Long Term Software Sustainability

Abstract

Software systems evolve continuously to meet changing user requirements, technological advancements, and business demands. During the evolution process developers often introduce shortcuts, incomplete implementations, or suboptimal design decisions in order to meet tight deadlines or resource constraints. These compromises accumulate over time and form what is commonly referred to as technical debt. While technical debt may provide short term development benefits, excessive accumulation can significantly reduce software quality, maintainability, and long-term sustainability. As modern software systems grow in size and complexity, managing technical debt has become a critical concern for software engineering practitioners and organizations. Technical debt prediction models have emerged as an important analytical approach for identifying potential areas of code that may generate high levels of technical debt in the future. These predictive models employ machine learning algorithms, static code analysis techniques, and historical repository data to forecast code quality degradation and maintenance risks. By enabling early identification of potential issues, such models support proactive decision making and resource allocation aimed at sustaining software quality over extended development cycles. This study analyzes the impact of technical debt prediction models on long term software sustainability. The research proposes a conceptual framework that examines the relationships between technical debt prediction accuracy, code quality improvement, maintenance efficiency, and software sustainability. A quantitative research design using structural equation modeling with SmartPLS is employed to evaluate the relationships between these constructs. The results demonstrate that accurate technical debt prediction models significantly improve code quality and maintenance efficiency, which ultimately contributes to enhanced long term software sustainability. The findings also indicate that organizations that integrate predictive analytics into their software development practices are better positioned to manage technical debt effectively. This research contributes to the field of software engineering by providing empirical evidence on the role of predictive analytics in sustainable software development. The proposed framework offers practical guidance for software development teams seeking to implement technical debt prediction models as part of their long-term software quality management strategies.