AI-Enhanced Configuration Management in CI/CD for Scalable and Automated Release Engineering Solutions
Keywords:
Configuration Management, CI/CD, AI-Driven Automation, Release Engineering, Scalability, Machine Learning, Anomaly Detection, Predictive ModelingAbstract
Configuration management (CM) plays a critical role in ensuring consistency, accuracy, and scalability within Continuous Integration and Continuous Deployment (CI/CD) pipelines, especially in large-scale software development environments. Integrating Artificial Intelligence (AI) into CM offers advanced, scalable solutions to automate configuration processes, reduce human error, and enhance system efficiency. This paper explores the application of AI-driven CM within CI/CD pipelines, detailing its impact on release engineering, deployment automation, and scalability. Through an examination of recent original research and empirical studies, this paper identifies AI techniques that enhance CM, such as machine learning models for predicting configuration drifts and AI-based anomaly detection in CI/CD workflows. The findings underscore AI’s capacity to foster an automated, resilient, and scalable approach to release engineering.
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Copyright (c) 2025 Mohamed Taufiq Bahar, Agustinus Sakti Afolabi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
