An AI-Augmented Framework for Continuous Quality in CI/CD Pipelines

Abstract

Modern software development teams are facing increasing pressures in preserving quality assurance in Continuous Integration and Continuous Deployment pipelines due to the rapidly growing size of test suites and frequency of deployment. This framework provides an AI-enhanced approach that adopts a proactive quality management approach to traditional reactive testing through three primary functional capabilities: intelligent test selection, predictive risk assessment, and automated anomaly detection with self-healing capabilities. The use of historical execution data and patterns of code change and defect correlations assists in the identification of the proper subset of tests to run while still maximizing defect detection rate and minimizing false negatives. Ensemble learning, combining logistic regression, gradient boosted trees, and deep neural networks, develops composite risk scores, with failure probability given to the changes before it is deployed to the production environment. Anomaly detection is performed by unsupervised learning using autoencoders and isolation forests that create baseline behavior models to monitor pipelines in real time. Reinforcement learning agents are used to optimize self-healing by automating processes for remediation of infrastructure failure and configuration drift. Integration into DevOps toolchains occurs through microservices architecture and webhook mechanisms for easy horizontal scaling and event-based processing. Evidence of effectiveness throughout enterprise settings shows marked gains in the efficiency of pipeline execution, defect detection, incident prediction accuracy, average time to resolution, and cost savings while improving time-to-market.