Architecting Autonomous Testing Pipelines for Cloud-Native Systems Using AI-Driven Fault Injection and Predictive Analytics
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P121Keywords:
Cloud-Native Systems, Autonomous Testing, AI-Driven Fault Injection, Predictive Analytics, CI/CD Pipelines, Resilience Engineering, Machine Learning, DevOpsAbstract
The rapid adoption of cloud-native architectures, characterized by microservices, containerization, and dynamic orchestration, has significantly increased the complexity of software systems. Conventional methods of testing do not always suffice to tackle the issues related to distributed environments, scaling dynamically and deploying continuously. In this paper, the author introduces an autonomous testing pipeline architecture which combines AI-based fault injection and predictive analytics to promote the reliability, resilience, and performance of a cloud-native system. The suggested model establishes a smart fault injection engine that can emulate realistic and adaptive failure modes according to past system dynamics and run-time environment. Simultaneously, a predictive analytics engine is used to predict possible failures, high-risk components, and prioritize test execution using machine learning models. These modules are easily incorporated in CI/CD pipelines by the use of a centralized test orchestrator that facilitates the execution of continuous, automated, and proactive tests in dynamic settings. Also, the architecture includes a monitoring and observability layer and a feedback and learning module that creates a closed-loop system that recursively optimizes testing strategies and promotes self-healing. Experimental evidence shows that it has a large improvement on fault-detection rates, prediction accuracy, and system resilience, without incurring a large performance overhead. The study will help to improve the intelligent DevOps in the future by facilitating self-adaptive and scalable efficient testing pipelines, which will lead to the development of robust and reliable delivery of cloud-native applications.
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