Predictive Defect and Dependency Analytics for Agile Software Project Management: Early Risk Identification and Dynamic Resource Reallocation

• Abbas Ahsun

  Texas University

  Author

Agile software project management faces persistent challenges in identifying defects early and dynamically reallocating resources to mitigate risks before they escalate. Traditional risk management approaches rely on subjective assessment and historical heuristics, often failing to capture emergent patterns in sprint data that signal impending quality issues. This study addresses the gap between predictive analytics capabilities and their practical integration into Agile workflows by developing a hybrid predictive framework that combines Gradient Boosting with Bayesian optimization for defect prediction and dependency risk assessment. Using retrospective data from 147 sprints across 12 software development projects, comprising 9,483 user stories, 3,742 identified defects, and 1,256 dependency records spanning 2022-2025, the research demonstrates that feature-level churn metrics, developer experience distributions, and dependency network centrality collectively achieve 89.4% prediction accuracy (F1: 0.87, AUC-ROC: 0.93), outperforming traditional static estimation methods by 24.3%. The proposed dynamic resource reallocation algorithm, evaluated through prospective simulation against historical baselines, reduced defect resolution lead time from a mean of 3.8 days to 2.3 days and improved sprint completion rates by 16.7%. The framework contributes a validated, replicable approach to evidence-based Agile decision-making, enabling project managers to proactively allocate testing and remediation resources based on predicted defect likelihood and dependency cascades. Practical implications include actionable dashboards for sprint planning and real-time resource adjustment protocols that can be integrated with existing Agile tools such as Jira and Azure DevOps.

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Copyright (c) 2026 Abbas Ahsun (Author)

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