A Literature-Grounded Holistic Sprint-Level Framework for Technical Debt Risk Prediction and Actionable Intervention in Agile Software Projects

Technical debt (TD) has become one of the most persistent challenges in modern agile software development. When development teams operate under tight sprint deadlines, they often make suboptimal design and implementation decisions that appear harmless in the short term but gradually erode software quality over time. Despite growing awareness of this problem, most existing approaches to managing technical debt remain reactive — teams typically address debt only after it has already accumulated, rather than identifying and preventing it early. This paper introduces a literaturegrounded holistic sprint-level framework designed to predict technical debt risk and recommend targeted intervention strategies in agile software projects. The framework combines technical metrics — including code churn, cyclomatic complexity, defect density, velocity deviation, refactoring frequency, and maintainability index — with non-technical indicators such as team burnout score, documentation completeness, and sprint planning accuracy within a unified predictive architecture. The framework conceptually incorporates two interpretable machine learning approaches — Logistic Regression and Random Forest — to support sprint-level technical debt risk classification into Low, Moderate, and High categories. Each risk level is associated with intervention recommendations derived from peer-reviewed literature. This study is theoretical in scope and grounded in secondary empirical synthesis. No real-world data collection, coding, or experimental validation was conducted. The proposed framework is conceptually supported through systematic synthesis of empirical findings from nineteen peer-reviewed studies. Empirical implementation and validation using real-world sprint datasets are identified as important directions for future work. The primary contribution of this paper is a unified theoretical framework that integrates technical and non-technical factors, interpretable machine learning approaches, and risk-driven intervention strategies to support proactive technical debt governance in agile software development environments.