AI-Augmented Test Automation Pipelines for Continuous Delivery in Large-Scale Software Systems
Keywords:
AI-augmented testing, continuous delivery, CI/CD pipelinesAbstract
Modern test automation must be dependable, adaptable, and low-touch to quickly expand CD activities in significant software systems. The study examines AI-augmented test automation pipelines employing ML models in CI/CD for autonomous software validation testing. Kubernetes, Jenkins, and GitHub-based DevOps ecosystems use supervised and reinforcement learning for intelligent test cases, adaptive test prioritization, anomaly detection, and predictive failure analysis. Dynamic microservices' pipeline robustness, feedback loops, and regression risk improve with AI-driven test orchestration. The study examines how self-healing test scripts and autonomous remediation maintain dependability during deployment modifications. ML-augmented testing boosts coverage, fault localization, execution speed, and containerized resources. The research suggests AI may transform test automation for scalable, data-driven, growing corporate software.
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