VALIDATING SOFTWARE UPGRADES WITH AI: ENSURING DEVOPS, DATA INTEGRITY AND ACCURACY USING CI/CD PIPELINES

Abstract

This study examines how AI algorithms might improve software upgrade validation in CI/CD pipelines for DevOps efficiency, data integrity, and accuracy. Supervised Learning (Neural Networks) predicts defects, Unsupervised Learning (Isolation Forest) detects anomalies, and Reinforcement Learning (Q-Learning) automates testing. Neural Networks predicted defects with 94% accuracy and 91% precision, Isolation Forests detected 89% anomalies for real-time monitoring, and Q-Learning decreased test execution time by 70%. A hybrid approach, combining the qualities of each technique, is the most reliable software validation method. Hybrid model development, real-time flexibility, scalability, security integration, and detailed real-world case studies are future directions.