Data-Driven Software Quality Assurance: Leveraging Machine Learning for Risk Prediction and Test Optimization
DOI:
https://doi.org/10.64137/3108-2637/IJMAR-V2I1P101Keywords:
Software Quality Assurance, Software Defect Prediction, Machine Learning, Risk Prediction, Test Optimization, Regression Testing, Observability, Mlops, Intelligent AutomationAbstract
Software quality assurance is undergoing a structural transition from manually orchestrated verification toward continuously adaptive, data-driven assurance. Contemporary software systems evolve under tight release cadences, heterogeneous architectures, volatile workloads, and increasing security exposure, making uniform testing strategies economically unsustainable and technically inefficient. This paper develops a literature-grounded research framework for data-driven software quality assurance that integrates machine learning based risk prediction with intelligent test optimization. The central argument is that modern quality engineering should no longer treat defect prediction, regression testing, observability, and governance as separate activities. Instead, these capabilities should operate as a closed feedback system in which code, process, runtime, and business-context signals are fused to prioritize the highest-risk components and allocate testing effort dynamically. Building on prior work in software defect prediction, test prioritization, observability, AI governance, enterprise automation, and domain-specific intelligent systems, the paper proposes a multi-layer architecture that links feature engineering, risk scoring, test selection, feedback learning, and operational controls. It further shows that patterns demonstrated in cloud-native pharmacy, healthcare, manufacturing, finance, and cyber-physical settings provide transferable insights for software assurance, particularly around traceability, secure microservices, anomaly monitoring, and deployment optimization. The paper contributes a unified conceptual model, a risk-to-test prioritization formulation, an implementation-oriented pipeline, and a research agenda for robust, explainable, and operationally grounded machine learning in quality engineering. The framework is intended for practitioners and researchers seeking to improve defect discovery efficiency, reduce waste in testing, and align software verification with real delivery risk.
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