AI-Driven Quality Assurance: Integrating Generative Models, Predictive Analytics, and Self-Healing Frameworks in Software Testing

This article investigates the transformative impact of artificial intelligence on software quality assurance practices, focusing on three critical innovations: generative AI for automated test script creation, machine learning-based predictive defect analytics, and self-healing test automation frameworks. Through a comprehensive analysis of implementation patterns across healthcare, fintech, and e-commerce sectors, the article demonstrates how these technologies collectively establish a continuous quality feedback loop that spans the entire software development lifecycle. The article examines how large language models facilitate contextually appropriate test case generation, how predictive algorithms identify high-risk code modules before deployment, and how adaptive frameworks mitigate maintenance overhead associated with evolving interfaces. The article reveals significant efficiency gains while highlighting implementation challenges related to ethical AI governance, toolchain integration, and effective human-AI collaboration in DevOps environments. This article contributes both theoretical frameworks and practical guidelines for organizations seeking to leverage AI technologies for enhanced software quality, providing a foundation for future research on test fairness metrics and sustainable automation practices.