International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Predictive Quality Analytics Using Machine Learning: A Case Study in Agile Software Development

Author(s) Jessy Christadoss, Uthra Santhanam, Dr. Manas Ranjan Panda
Country United States
Abstract Background & Motivation: Agile development's rapid cycles and evolving requirements challenge traditional QA methods, leading to inefficiencies in defect detection and increased post-release risks. These challenges necessitate intelligent, adaptive QA strategies that prioritize efforts effectively.
Objective: This study proposes a machine learning (ML)-based framework to predict defect-prone software modules early in Agile workflows, aiming to optimize testing, reduce manual QA overhead, and sustain product reliability.
Methods: Historical defect data and static code metrics (e.g., cyclomatic complexity, lines of code, code churn) were used to train supervised ML models. Among various classifiers, XGBoost demonstrated superior performance. Preprocessing included feature normalization and SMOTE for class imbalance. Model evaluation employed precision, recall, F1-score, AUC, and 10-fold cross-validation.
Results: The XGBoost model achieved an average F1-score of 0.87 and AUC of 0.91, accurately identifying over 80% of defect-prone modules. This enabled targeted QA, reducing overall testing effort by ~25% without compromising defect detection. The model also highlighted key predictive code metrics.
Impact: Integrating ML-driven defect prediction into Agile QA enhances both efficiency and quality. The approach supports proactive, data-informed testing strategies, contributing to continuous quality improvement in Agile software engineering.
Keywords Predictive Analytics, Defect Prediction, Machine Learning, Agile QA, Software Quality
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-16
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.57190
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E-ISSN 2582-2160
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